adaptive neuro fuzzy inference system (ANFIS)

The phenomenon of climate change, caused by both anthropogenic and natural factors, makes the forecasting of future climate and its impact on the proper management of agriculture, water, and soil resources, as well as watershed management, the environment, and desertification control, crucial. Accordingly, this study investigates future temperature and solar radiation in arid (Mashhad and Ahvaz) and semi-arid (Kermanshah) climates in Iran. First, daily climatic data for the baseline period (1991-2020) were obtained from synoptic stations in the study areas. Subsequently, temperature and solar radiation projections were generated for the future periods of 2021-2040, 2041-2060, and 2061-2080 using LARS-WG version 8, based on the HadGEM3 climate model under the SSP scenarios. The model's high accuracy in downscaling and its excellent performance in predicting climatic parameters across all stations were validated by high R² values (99%) and NS efficiency coefficient (99%), alongside low RMSE values (less than 30%). Results indicated that, compared to the 30-year baseline period, the average maximum temperature over the next 60 years is projected to increase by 0.37, 1.48, and 2.73°C (Mashhad); 1.09, 1.47, and 2.3°C (Ahvaz); and 1.3, 1.75, and 2.65°C (Kermanshah) under the SSP126, SSP245, and SSP585 scenarios, respectively. Similarly, the average minimum temperature is expected to rise by 1.01 °C, 1.89 °C, and 2.8 °C (Mashhad); 1.57 °C, 2.17 °C, and 3.24 °C (Ahvaz); and 1.8 °C, 2.43 °C, and 3.33 °C (Kermanshah), respectively. However, changes in mean annual solar radiation did not show a consistent pattern. The monthly trends for temperature and radiation were significant at a 95% confidence level for most months. The results suggest that future temperature increases may lead to a decline in the quantity and quality of agricultural products, reduced water resources, and increased soil erosion. Future changes in solar radiation will also affect photosynthesis, evapotranspiration, energy production, and fossil fuel consumption. Therefore, to mitigate the negative impacts and adapt to future climatic conditions in the study areas, managers and planners should adopt optimal strategies. These strategies include cultivating heat-and light-resistant crops, optimizing irrigation systems, designing watershed management systems to prevent water loss, and promoting sustainable land development.

Interference System (ANFIS), to predict the weld metal deposition in the Metal Active Gas (MAG) welding process for a given set of welding parameters using hybrid learning algorithm to have a correct amount of weld metal deposition to meet the correct welding requirement. Total 81 nos. of experiments are designed according to full factorial design of experiments with varied input parameters and its results are used to develop an ANFIS model. Multiple sets of data from experiments are utilized to train, check and validate the intelligent network, which is used to predict the amount of weld metal deposition. The proposed ANFIS, developed using MATLAB functions, is flexible, and it scopes for a better online monitoring system to achieve better welding requirement.

This study evaluates the predictive accuracy of Regression Trees (RTrees) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for estimating the carbon footprint in residential construction projects. The results indicate that the ANFIS significantly outperforms the RTrees in predictive accuracy, achieving a reduction in Root Mean Square Error (RMSE) by 84.3% in the production stage (from 0.53174 to 0.08346) and by 40.4% in the operational stage (from 0.13865 to 0.08265). These improvements underscore the effectiveness of the ANFIS in capturing complex nonlinear relationships in carbon footprint data. Despite its superior accuracy, the ANFIS exhibits higher computational costs, requiring an average training time of 76.2 s, compared to 12.4 s for the RTrees. These findings highlight the trade-offs between accuracy and computational efficiency, providing valuable insights for selecting machine learning models in sustainable construction. The study concludes that integrating hybrid approaches or ensemble learning could further enhance predictive performance while maintaining efficiency.

Slope stability analysis is crucial in civil engineering for the design and maintenance of embankments, especially those constructed on soft soils. Traditional methods like the limit equilibrium method (LEM) and finite element method (FEM) are timeconsuming and require significant expertise. This study explores the application of three machine learning models-Artificial Neural Network (ANN), ANN combined with Imperialist Competitive Algorithm (ANN-ICA), and Adaptive Neuro-Fuzzy Inference System (ANFIS)-to predict slope stability. A numerical analysis using PLAXIS 2D software generated a database encompassing various geometric characteristics such as slope height, surcharge, and slope angle. These features served as input parameters, while the factor of safety (FOS) values were used as target outputs. The performance of each model was evaluated using determination coefficients (R²) and root mean square errors (RMSE). The ANN-ICA hybrid model demonstrated superior predictive accuracy, with R² and RMSE values of 0.998 and 0.041 for training datasets, respectively, outperforming the standalone ANN (R² = 0.724, RMSE = 0.124) and ANFIS (R² = 0.858, RMSE = 0.052) models. This study highlights the potential of hybrid machine learning approaches in enhancing the efficiency and accuracy of slope stability predictions, offering a promising alternative to traditional methods.

Swelling in compacted soils may lead to some damages to structures and buildings. For the sake of reducing such damages, soil swelling should be determined, so as to make the structures exhibit adequate resistance against such a phenomenon. For most cases, fully non-linear relations have been observed between soil swelling and the parameters contributing to swelling in compacted soil. As such, soil swelling should be determined via either experimentations or prediction models. However, being extremely timely, swelling tests require special expensive equipment. Accordingly, there is a need for models which can use available data to theoretically give swelling estimations of a relatively high accuracy without getting busy with swelling tests and associated issues. Investigated and evaluated in this research are the ability and application of an adaptive neuro-fuzzy interference system (ANFIS) developed by subtractive clustering and fuzzy c-mean clustering to determine and predict swel...

Load forecasting plays an important role in today's electricity grid, due to the presence of distributed generation. In this paper, various methods for short-term load forecasting are presented with the purpose to serve as tool to operate within a demand side management system. The need for large amount of historical data is avoided, opening the possibility for immediate use by customers without recorded load history. Widely distributed, such system can increase the reliability of the modern grid. The results showed that the Holt-Winters forecasting procedure give better results in comparison to neural networks with gradient descent, principally if the load rises fast in the early morning hours.

Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses.

The use of statistical and empirical models for the prediction of surface roughness always lacks generalizability and is not reliable when applied to unseen datasets. To increase generalizability and accuracy in predicting responses, researchers developed artificial intelligence and machine learning. The objective of this research is to develop advanced machine learning to predict surface roughness. For instance, the decision tree (DT), adaptive boosting (ADB), gradient boosting (GB), and extreme gradient boosting (XGB)s are used to predict surface roughness. The extreme gradient boosting (XGB) outperforms the other models by scoring the highest regression coefficient (R 2 = 98.7%) and least mean absolute error (MAE = 7.4%), least mean absolute percentage error (MAPE = 3.58%), and least root mean square error (RMSE = 8.0%). Further, based on the SHAP, the coolants have higher effects on surface roughness followed by cutting speed and feed rates respectively, and the tool overhangs have the least effects. Increasing cutting speed and coolant, decreases surface roughness while increasing feed rate and tool overhangs decreases surface roughness. The extreme gradient boosting (XGB) has highest generalizability and can be applied to every section of industrial and manufacturing sector for prediction of surface roughness; thus, it can save time and resource.

Traffic signs are among the most important traffic equipment that are used in urban and non-urban areas and their purpose is to increase road volume and reduce delays while ensuring safe movement. Over the years, due to the growing trend of car production and car use, which has increased urban and road traffic, traffic signs have increased and become more diverse and efficient. One of these traffic control methods at intersections is the use of traffic signal scheduling techniques, which despite the advantages of this method has a major drawback, and that is that due to the dynamic behavior of traffic, this method has issues in predicting traffic signal timing especially in times of peak traffic. Designing appropriate green times for traffic signal lights with Adaptive Neuro-Fuzzy Inference System (ANFIS) technique in traffic signal controller is a feasible solution to tackle this issue in urban network congestion during peak hours. The capability to learn from experience is one of ...

Fuzzy logic systems (FLS) provide a strong paradigm to model, reason, and make decisions in environments of uncertainty, imprecision, or incomplete information. Unlike classical Boolean logic with truth values limited to 0 or 1, fuzzy logic enables partial membership and continuous transition of truth values, thus enabling one to model real-world phenomena in a more proximate manner. This paper is a comprehensive overview of fuzzy logic systems: from the theoretical foundationsfuzzy sets, membership functions, inference engines, and defuzzification methods-to other fuzzy logic models (Type-1, Type-2, Mamdani, Takagi-Sugeno). We cover recent developments in hybrid systems such as neuro-fuzzy, evolving fuzzy systems, and fuzzy rule-based systems optimized using heuristic and evolutionary methods. Through case studies of robotics (robot navigation in mobile robots and swarm robotics), bioenergy technology, wastewater treatment, hydraulic and pneumatic systems, and fault-tolerant control, we illustrate in practice the application of fuzzy logic, its performance, and compromises. Compromises among gains in interpretability, flexibility in handling uncertainty, and adaptability are balanced against computational complexity, rule base explosion, expert-dependence, and realtime implementation constraints. Trends and developments covered include advancements in Type-2 fuzzy logic, meta-heuristics parameter tuning, and uncertainty quantification for safety-critical tasks. Our view is that with increasingly complex applications (IoT, autonomous systems, environment monitoring), fuzzy logic systems-especially advanced ones-are becoming increasingly relevant. This paper aims to assist researchers in making the right choice of FLS architectures, designing efficient inference and defuzzification methods, and navigate through some of the current challenges in applying fuzzy logic to modern complex systems.

The performance of an off-grid solar Photovoltaic (PV) system with Battery Energy Storage (BES) depends on the system's location. Incorporating climatic variables such as solar irradiance, ambient and cell temperatures into the modelling of PV systems helps predict the system's appropriate behaviour. The paper discusses the impact of variations in seasonal irradiance, temperature, and system location on the optimum sizing of a standalone solar PV-BES system for minimum total annual cost using Improved Particle Swarm Optimization. Three locations: Pune (Maharashtra), Ladakh (Jammu and Kashmir), India, and Rafsanjan (Iran) with extreme weather conditions (winter and summer) are identified to analyze the effect for optimum sizing of PV-battery energy systems. In addition, the optimum system sizing is analyzed for lead acid and lithium-ion batteries for both seasons. The results indicate that the system's size and cost are significantly affected due to changes in location, temperature, and seasonal irradiance. Assuming the same load demand in both seasons, the number of lithium-ion batteries required is less than that of lead acid batteries, proving their cost-effectiveness. The study gives a comprehensive techno-economic analysis of the PV-BES system considering climatic variations at three locations for two battery chemistries.

In this study, stiffness modulus parameters of asphalt concrete were determined experimentally for different temperature and exposure times. The stiffness modules were calculated according to Nijboer stiffness module. Basic physical properties and the quantity of bitumen of asphalt core samples were designated for determining the stiffness modules. The samples were exposed to 17 °C (reference temperature), 30, 40 and 50 °C temperatures for 1.5, 3, 4.5 and 6 h respectively and then Marhall Stability tests were done for each samples. By using the test results a prediction model with Sugeno type based on the adaptive neuron-fuzzy inference system (ANFIS) was alternatively developed to predict the stiffness modules of asphalt core samples. As a result, it was seen that the developed prediction model could be used as a prediction model for unperformed situations which are not suitable for experiments.

Precise forecasting of reference evapotranspiration (ET0) is one of the critical initial steps in determining crop water requirements, which contributes to the reliable management and long-term planning of the world’s scarce water sources. This study provides daily prediction and multi-step forward forecasting of ET0 utilizing a long short-term memory network (LSTM) and a bi-directional LSTM (Bi-LSTM) model. For daily predictions, the LSTM model’s accuracy was compared to that of other artificial intelligence-based models commonly used in ET0 forecasting, including support vector regression (SVR), M5 model tree (M5Tree), multivariate adaptive regression spline (MARS), probabilistic linear regression (PLR), adaptive neuro-fuzzy inference system (ANFIS), and Gaussian process regression (GPR). The LSTM model outperformed the other models in a comparison based on Shannon’s entropy-based decision theory, while the M5 tree and PLR models proved to be the lowest performers. Prior to perfor...

Today, neural network models are widely used to predict whether a person will develop diabetes in the future. However, for fuzzy inference engine and Adaptive Network-based Fuzzy Inference System (ANFIS), it costs a lot when the number of features is large, and the model will be more explainable if we use feature selection methods. In this paper, we modify the ANFIS model, combine ANFIS and neural network, and propose the ANFIS-NN model. We use SMOTE to address the imbalance between different classes, and use RFE and Casual Inference to do feature selection work. Then, we train an ANFIS model, and use a 5-layer neural network to replace the last layers to improve prediction accuracy. Data comparison experiments shows that our models get 0.9812 on accuracy, 0.9790 on G-mean, 0.9776 on F1 score on PIMA dataset using Casual Inference feature selection method, much better than traditional ANFIS; and our model works better on other diabetes datasets. The ANFIS-NN proposed in this paper c...

The flow in sewers is a complete three phase flow (air, water and sediment). The mechanism of sediment transport in sewers is very important. In other words, the passing flow must able to wash deposited sediments and the design should be done in an economic and optimized way. In this study, the sediment transport process in sewers is simulated using a hybrid model. In other words, using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Particle Swarm Optimization (PSO) algorithm a hybrid algorithm (ANFIS-PSO) is developed for predicting the Froude number of three phase flows. This inference system is a set of if-then rules which is able to approximate non-linear functions. In this model, PSO is employed for increasing the ANFIS efficiency by adjusting membership functions as well as minimizing error values. In fact, the PSO algorithm is considered as an evolutionary computational method for optimizing the process continues and discontinues decision making functions. Addition...

Summary Variation in the colour of dried tomatoes is frequently a problem for both consumers and processors. This study investigated digital imaging and applied soft-computational modelling using the Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) to evaluate the surface colour of microwave-dried tomato slices. The tomatoes were pretreated with water blanching, ascorbic acid, and sodium metabisulphite, then cut into slices of 4, 6, and 8 mm thickness. The slices were then dried in a microwave oven at power levels of 90, 180, and 360 W. The colour characteristics of the dried tomato slices (L*, a*, b*, colour change, browning index, hue, and chroma) were determined. The response variables were modelled and optimised using ANN and ANFIS. The efficiency and performance of the model were assessed using the coefficient of determination (R2), the root means square error (RMSE), and the mean absolute error (MAE). The results revealed the ranges of 36.70 – ...

http://jpst.ripi.ir Journal of Petroleum Science and Technology 2019, 9(3), 10-26 © 2019 Research Institute of Petroleum Industry (RIPI) ABSTRACT Fluid catalytic cracking (FCC) process is a vital refinery process which majorly produces gasoline. In this research, a hybrid algorithm which was constituted of Adaptive Neuro-Fuzzy Inference System (ANFIS) and firefly optimization algorithm was developed to model the process and optimize the operating conditions. To conduct the research, industrial data of Abadan refinery FCC process were carefully gathered along a definite period. Then a model based on ANFIS was developed to investigate the effect of operating variables including reactor temperature, feed flow rate, temperature of top of main column, and the temperature of bottom of the debutanizer tower on quality and quantity of gasoline, LPG flow rate, and process conversion. Moreover, statistical parameters comprising R2, RMSE, and MRE approved the accuracy of the model. Eventually,...

At the computational point of view, a fuzzy system has a layered structure, similar to an artificial neural network (ANN) of the radial basis function type. ANN learning algorithms can be employed for optimization of parameters in a fuzzy system. This neuro-fuzzy modeling approach has preference to explain solutions over completely black-box models, such as ANN. In this paper, we implement the design of experiment (DOE) technique to identify the significant parameters in the design of adaptive neuro-fuzzy inference systems (ANFIS) for stock price prediction.

This paper presents the construction of a system suitable for student diagnosis with the use of neurofuzzy techniques. To be more specific, the extent of usefulness of Adaptive NeuroFuzzy Inference Systems (ANFIS) is examined for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the specific methods is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process. The system was trained through data extracted from an educational project called ENFORA.

Banking risk measurement and management remain one of many challenges for managers and policymakers. This study contributes to the banking literature and practice in two ways by (a) proposing a risk ranking index based on the Mahalanobis Distance (MD) between a multidimensional point representing a bank’s risk measures and the corresponding critical ratios set by the banking authorities and (b) determining the relative importance of a bank’s risk ratios in affecting its financial standing using an Adaptive Neuro-Fuzzy Inference System. In this study, ten financial ratios representing five risk areas were considered, namely: Capital Adequacy, Credit, Liquidity, Earning Quality, and Operational risk. Data from 45 Gulf banks for the period 2016–2020 was used to develop the model. Our findings indicate that a bank is in a sound risk position at the 99%, 95%, and 90% confidence level if its Mahalanobis distance exceeds 4.82, 4.28, and 4.0, respectively. The maximum distance computed for ...

This paper introduces a new ANFIS adaptive neurofuzzy inference model for laser surface heat treatments based on the Green's function. Due to its high versatility, efficiency and low simulation time, this model is suitable not only for the analysis and design of control systems, but also for the development of an expert real time supervision system that would allow detecting and preventing any failure during the treatment.

Neurobiology is to study of cells of the nervous system of human being and we can organize these cells into functional circuits. In turn it processes information and mediates behavior. This procedure can also be thought as a decision making process. Actually it is known that Neurons are cells that are specialized to receive, propagate, and transmit electrochemical impulses.The field of cognitive neuroscience concerns the scientific study of the neural mechanisms i.e. function of neuron underlying cognition and is a branch of neuroscience.This paper investigates behavior of cells in decision making process.

Rating Curve is a equation to describe correlation between of the stage-discharge in AWLR (Automatic Water Level Recorder). This equation (formula) is important for the planning of water resources and hidrology model. The equation in AWLR on the site Pantai Cermin Surb-Watershed Siak Hulu in years of 2002-2009 is not-available because it hasn't data of the rating curve equation based on it, This research was developed to predict the rating curve equation on the site Pantai Cermin.This Research using Matlab R2013a software as a tool with ANFIS Sugeno Model. The data in ANFIS Sugeno Model using combination between data of stage-discharge in years of 2002-2006. The output of that is the data discharge in year of 2008. Result of this research with using of these data that it consist of 70% of the training data, 30% of the testing data, and 100% of validation datais a very good of the correlation value. The correlation values of this research are 0.99999, 0.99998, and 0.99999. The eq...

The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module’s operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathem...

Cruise control systems are of great importance in modern cars, not only to save energy but also to enhance driving comfort, avoid road accidents and achieve safety. The speed control system is a multivariable system with high nonlinearity, which makes the use of conventional controllers, such as PID, infeasible under all conditions. This research deals with the design and analysis of an ANFIS-based controller and its comparison with other controllers such as PID, fuzzy logic, and model predictive controllers of the vehicle speed control system. The MATLAB/SIMULINK environment was used to model and simulate the cruise control system to identify the efficiency of the four controllers and show the comparison between them. This work presents the design and performance analysis of an intelligent controller for a vehicle cruise control system, comparing it with PID, ANFIS and model predictive control techniques. Simulation results show that FLC, ANFIS, and MPC controllers have different strengths, with trade-offs between fast response, overshoot, and integral error. The results underscore the importance of selecting the appropriate controller based on specific application requirements, with MPC showing superiority in managing constraints.

This paper presents an optimal control system for enhancing road feel in a steer-by-wire (SbW) system using fuzzy logic control (FLC) optimized with modified quantum particle swarm optimization (MQPSO). The objective is to improve the driver experience by providing realistic torque feedback, thereby replicating the steering sensations typically generated by road conditions. This feedback is essential for conveying information about vehicle dynamics and road surface variations through opposing torque applied to the steering interface. An artificial intelligence-based control system utilizing FLC was developed to manage the road feel feedback within the SbW system. The inputs to the FLC include steering angle, vehicle speed, and steering ratio, as well as key physical factors such as inertia and friction, all of which influence the generation of steering torque. The FLC parameters were optimized using MQPSO to achieve a more accurate and responsive road feel torque output. A Simulink model was constructed to simulate the proposed system. The simulation results demonstrate that the optimized FLC significantly improves the performance of the steering motor torque feedback mechanism. This study contributes to the advancement of steer-by-wire technology by proposing an optimal torque control framework and highlighting the effectiveness of integrating FLC with MQPSO in enhancing road feel dynamics.

The performance of data mining techniques has been proven accurate in many studies, but each method in  data  mining  techniques  has  different  accuracy  depending  on  the  type  of  data  that  is  the  object  of  research. Methods in data mining techniques are divided into several functions, namely: clustering, association, classification, and  prediction,  where  each  data  mining  technique  objective  has  a  superior  method.  Therefore,  in  this  case  the author will compare the performance of the multiple linear regression method, and neural networks with fuzzy mamdani in predicting the income of PLN Unit Takengon. In several studies, the Backpropagation method shows the highest accuracy compared to other methods. Then the prediction model with multiple linear regression also has the highest accuracy as well as the Fuzzy Mamdani method has high accuracy too. Therefore, the purpose of this  study  is  to  compare  the  three  methods,  so  that  it  can  be  determined  which  method  has  a  higher  accuracy value. The results of this study indicate that the Back propagationmethod has the highest accuracy and the lowest average error, namely a MAPE value of 5.9% with an accuracy of 94.1% and an RMSE of 14398.14, followed by the multiple linear regression method obtaining a MAPE value of 6.9% with an accuracy of 93.1% and an RMSE of 15527.41, then for Fuzzy Mamdani obtaining a MAPE value of 7% with an accuracy of 93% and an RMSE of 16077.76.

Roadblocks in Bhutan are common and significant challenges that impact transportation, public safety, and the economy. Predicting these roadblocks is difficult because of the complex interplay between geological, climatic, and topographical factors. This research proposes to develop a predictive model using Adaptive Neuro-Fuzzy Inference System (ANFIS) to create a roadblock susceptibility map of Bhutan. The study incorporates fourteen influencing factors such as rainfall, soil type, elevation, slope, aspect, settlement area, profile curvature, plane curvature, distance to rivers, distance to fault, topographic position index (TPI), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI) and Normalized Difference Vegetation Index (NDVI), with roadblock inventory data. These factors were processed using Quantum Geographic Information System (QGIS) to build a geodatabase. The data are split into 70% for training the ANFIS model and 30% for validating the results. The ANFIS model incorporates the neural networks and fuzzy logic principles and it gives better predictions. The performance accuracy, evaluated using the confusion matrix, was 0.8408, indicating good predictive ability. The Analytical Hierarchy Process (AHP) was used to determine the relative weight of each factor, which was then applied in the Weighted Over Method (WOM) to create a susceptibility map. This map, generated for both the entire country and individual districts, can aid in mitigation efforts and serve as a preliminary tool for future infrastructure planning.

Research of position control of 1-DOF high-precision rotary table using adaptive Neuro-Fuzzy inference system (ANFIS) controller has been done. In the closed-loop system without a controller, the response was oscillating and pounding caused by inertial torque. It because a rotary table receives a considerable load. Based on this, the ANFIS controller is needed to eliminate oscillations and compensate for the inertia. The result shows that there was no oscillation or overshoot with the steady-state error value of 2.27% for the reference angle of 45°, valued at 0.10% reference angle of 180°, and valued at 0% reference angle of 360°. The result proves that ANFIS controllers can eliminate oscillations with and compensate for inertia.

Adaptive control is the capability of a control system to modify its operation and achieve the best possible operation mode. A quadcopter is a nonlinear, unstable and under-actuated dynamic system, thus providing a challenge to control engineers in controlling and stabilising it during flight. This paper proposes the design, development, and application of an intelligent adaptive hybrid controller to control and stabilise the drone. The training data for adaptive neuro-fuzzy inference systems (ANFIS) are generated by the Linear Quadratic Regulator (LQR) under white-noise disturbance. The trained ANFIS is subsequently used to estimate the parameters of the control distribution matrix for the actual fault condition and the reconfiguration is carried out by computing new feedback gain using the pseudo-inverse technique. For the simple adaptive controller, LQR is also used to generate the desired trajectories of the reference model. In both experiments, the extended Kalman filter is imp...

This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and Ethereum Dominance (ETH.D) in a daily timeframe. The methods used to teach the data are hybrid and backpropagation algorithms, as well as grid partition, subtractive clustering, and Fuzzy C-means clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed in this paper has been compared with different inputs and neural network models in terms of statistical evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time.

The partner village development program (PPDM) has been carried out about the production of ant sugar from coconut sap in the village of Wonosobo, Banyuwangi Regency as a college partner. This activity is intended to assist government programs in the development of coconut sugar products into Healthy Coconut Sugar (Helicos) products. This is expected to be the main icon and driver of the Village to become the center of Helicos products. The service program activities are carried out on April 16 - July 4, 2018. In the implementation of this activity, participants who work as print palm sugar producers participated. With this activity, partners get new understanding and skills in making innovation-based palm sugar. Community service is carried out by coaching partners through diversification of coconut sugar products into Helicos ants, improving product quality by applying hygine-sanitation, K3 during harvesting, processing and storage. The results of the dedication program can be see...

This paper presents a study on optimization of process parameters using particle swarm optimization to minimize angular distortion in 202 grade stainless steel gas tungsten arc welded plates. Angular distortion is a major problem and most pronounced among different types of distortion in butt welded plates. The process control parameters chosen for the study are welding gun angle, welding speed, plate length, welding current and gas flow rate. The experiments were conducted using design of experiments technique with five factor five level central composite rotatable design with full replication technique. A mathematical model was developed correlating the process parameters with angular distortion. A source code was developed in MATLAB 7.6 to do the optimization. The optimal process parameters gave a value of 0.0305° for angular distortion which demonstrates the accuracy of the model developed. The results indicate that the optimized values for the process parameters are capable of producing weld with minimum distortion.

The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.

Modern wireless systems are placing greater emphasis on antenna designs for future development in communication technology because the antenna is a key element in the overall communication system. A Microstrip Antenna is well suited for wireless communication due to its light weight, low volume and low profile planar configuration which can be easily conformed to the host surface. In this paper, an optimization method based on adaptive neuro-fuzzy inference system (ANFIS) for determining the parameters used in the design of a rectangular microstrip patch antenna is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system (FIS) and the learning capability of artificial neural network (ANN). By calculating and optimizing the patch dimensions and the feed point of a rectangular microstrip antenna, this paper shows that ANFIS produces good results that are in agreement with Ansoft HFSS 13.0 simulation results.

The Interval Type-2 Fuzzy Logic Control (IT2FLC) utilizes a genetic algorithm (GA), known as the Genetics Interval Type-2 Fuzzy Network (GIT2FS), to optimize the fuzzy parameters, including fuzzy functions for membership and fuzzy regulation bases. After a brief discussion of the genetic fuzzy system GFS, the suggested design is described. Type reductions and defuzzification are included in the output processing of interval type-2 fuzzy logic circuits. Although researchers have recently developed numerous effective type reduction techniques, there are currently no practical plans to enhance the output of defuzzification. The kind of interval type-2 fuzzy set is reduced using the type reduction algorithm presented in this paper, which also produces the best defuzzified output from the type-reduced set. The planned type reduction is also carried out offline (in other words, the controller has been reduced to type-1 in practical applications). It greatly lowers the computational expense and makes it easier, actually, to develop controllers. Problems with truck backing control are used to show the viability of the suggested approach. The study showed that, in terms of speed, computational complexity, and resilience, the suggested technique performs better than typical IT2-FLCs.

Wind energy power (WEP) is currently one of the generating technologies that could be implemented massively due to its low environmental impact and abundant resources. However, the availability of the wind always changes depending on the weather condition, such that the power system should be designed properly to adopt the intermittence of the power injection of WEP. Therefore, the wind speed forecasting is very prominent to be performed to ensure the WEP could be incorporated into the existing power system. In this paper, the proposed methods to predict the wind speed are developed based on the artificial intelligence methods i.e. Adaptive Neuro Fuzzy Inference System (ANFIS) and based on the conventional methods i.e. Auto Regressive Integrated Moving Average (ARIMA). Case study in Malang city of Indonesia has been taken to compare the performances of both methods. Some membership functions (MF) have been studied to show the performance of ANFIS. The mean absolute error (MAE) and root mean square error (RMSE) have been used as standard statistical metrics to measure the performance of ANFIS and ARIMA methods. The results show that the optimal ANFIS architecture was obtained with 85% training data and 15% testing data by using the Generalized Bell membership function with MAE of 2.1354 km/h and RMSE of 2.6333 km/h. In addition, the wind forecasting result using ARIMA has been obtained with MAE of 2.8383 km/h and RMSE of 3.4628 km/h. The ANFIS method offers better performance than ARIMA does for short-term forecasting of wind speed in terms of MAE and RMSE values.

Handwriting recognition is one of research which is using many kinds of algorithm. Some researcher using artificial neural network, while others is using fuzzy logic. This research use one of hybrid system which combines artificial neural network and fuzzy logic. This system is called Adaptive Neuro Fuzzy Inference System, or well known as ANFIS. ANFIS combine capability of fuzzy logic for reasoning and artificial neural network for learning data and pattern. For these reasons, ANFIS is used to recognition system for handwriting image. ANFIS, in this research is build for learning and recognizing numerical handwriting image. In learning phase, this system using Recursive Least Square Estimator for feed forward algorithm, and steepest descent for the feed back, as for recognizing phase, only one iteration feed forward only is done without any learning algorithm anymore because consequent and premise parameter has been recorded in learning phase. ANFIS are implemented to recognize numerical handwriting using Sugeno method as its fuzzy rule base and its network consist of 5 layers; 2 adaptive layers and 3 static layers. From the result of the recognizing test, acuration for this ANFIS recognition system is 62.5%. This means from 40 images to be recognized, 25 images are success and 15 are failing to recognize. For every phase of the trial which is done in 5 trial phase, the accuration result is the same, 62.5%. These are caused by the different amount of the training data is not much so the variant is not too great as well.

This paper presents a new adaptive learning algorithm to automatically design a neural fuzzy model. This constructive learning algorithm attempts to identify the structure of the model based on an architectural self-organization mechanism with a data-driven approach. The proposed training algorithm self-organizes the model with intuitive adding, merging and splitting operations. Sub-networks compete to learn specific training patterns and, to accomplish this task, the algorithm can either add new neurons, merge correlated ones or split existing ones with unsatisfactory performance. The proposed algorithm does not use a clustering method to partition the input-space like most of the state of the art algorithms. The proposed approach has been tested on well-known synthetic and real-world benchmark datasets. The experimental results show that our proposal is able to find the most suitable architecture with better results compared with those obtained with other methods from the literature.

This study aims to optimize algorithmic trading strategies using the relative strength index (RSI) and the moving average convergence divergence (MACD) indicators in the Vietnamese stock market. An automated trading system is constructed to optimize indicator parameters using multi-objective particle swarm optimization (PSO) over three objective functions: total return, win rate, and number of trades. The system employs simultaneous optimization of parameters and signal aggregation for developing the optimal selection strategy. Based on daily Vietnam index data from 2018 to 2024, the results show that the PSO method surpasses the differential evolution (DE) method in both returns and execution time. Additionally, the optimal selection strategy achieves superior performance compared to benchmark strategies. It also demonstrates the ability to adapt to the preferences of traders by selecting appropriate indicators. Traders can use the MACD indicator to seek higher profits, while the RSI indicator is more suitable for minimizing transaction costs in a volatile market.

Two of the major challenges associated with time series modelling are handling uncertainty present in the data and tracing its dynamical behaviour. A Recurrent Interval Type 2 Fuzzy Inference System or RIT2FIS is presented in this paper. RIT2FIS adopts an interval type 2 fuzzy inference mechanism for superior handling of uncertainty. The memory neurons employed in its hidden and output layer, retain the temporal information, making RIT2FIS highly proficient in tracing system dynamics at a granular level. RIT2FIS also benefits from incorporating a k-means algorithm inspired approach to cluster the data in an unsupervised manner. An 'Elbow Method' is utilized next to determine the optimal clustering which is then employed as the optimal fuzzy rule base for RIT2FIS, eliminating the necessity of expert knowledge for fuzzy initiation. The antecedent and consequent parameters of RIT2FIS are updated using a gradient descent based backpropagation through time algorithm where the learning is made self-regulatory to avoid over-fitting and ensure generalization. Performance of RIT2FIS is evaluated against popular neuro-fuzzy methods on different benchmark and real-world time series problems which distinctly indicates an improved accuracy and a parsimonious rule base.

Renewable energy is fast becoming a mainstay in today's energy scenario. One of the important sources of renewable energy is the wave energy, in addition to wind, solar, tidal, etc. Wave prediction/forecasting is consequently essential in coastal and ocean engineering studies. However, it is difficult to predict wave parameters in long term and even in the short term due to its intermittent nature. This study aims to propose a solution to handle the issue using Interval type-2 fuzzy inference system, or IT2FIS. IT2FIS has been shown to be capable of handling uncertainty associated with the data. The proposed IT2FIS is a fuzzy neural network realizing Takagi-Sugeno-Kang inference mechanism employing meta-cognitive learning algorithm. The algorithm monitors knowledge in a sample to decide an appropriate learning strategy. Performance of the system is evaluated by studying significant wave heights obtained from buoys located in Singapore. The results compared with existing state-of-the art fuzzy inference system approaches clearly indicate the advantage of IT2FIS based wave prediction.

Caching performance can be improved by designing good replacement policies. This paper, discuss the various approaches that were designed based on genetic algorithms and fuzzy logic to optimize the performance of caching. The approaches discussed here proved to be more effective in solving the problems as compared to the conventional techniques that were used earlier in this problem domain. Keywords-Genetic algorithms; Cache replacement; Fuzzy

Accurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combina...

Energy production and distributing have critical importance for all countries especially developing countries. Studies about energy consumption, distributing and planning have much importance at the present day. In order to manage any power plant or take precautions about energy subject, many kinds of observations are used for short, mid and long term forecasting. Especially long term forecasting is in need to plan and carry on future energy demand and investment such as size of energy plant and location. Long term forecasting often includes power consumption data for past years, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Long term forecasting data vary from one month to several years. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression models. In this study, artificial intelligence is used to forecast long term energy demand. Artificial intelligences are widely used for engineering problems to solve and obtain valid solutions. Adaptive neural fuzzy inference system is one of the most famous artificial intelligence methods and has been widely used in literature. In addition to numerical inputs, Adaptive neural fuzzy inference system has linguistics inputs such as good, bad and ugly. Adaptive neural fuzzy inference system is used to obtain long term forecasting results and the results are compared to mathematical methods to show validity and error levels. In order to show error levels, mean absolute error and mean absolute error percentage are used. Mean absolute error and mean absolute error percentages are very common and practical methods in literature. The obtained error results, from 2003 to 2025, mean absolute error and mean absolute percentage error are 1.504313 and 0.82439, respectively. Success of Adaptive neural fuzzy inference system for energy demand forecasting is 99.17%.

Purpose A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models. Design/methodology/approach These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO2) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were c...

The ongoing argument around intelligence continues to spark debates. The widespread use of intelligence testing in schools and other settings to determine specific abilities has caused controversy. Considering cultural and individual cognitive, psychomotor, and affective domains has paved the way for alternative models and theories to challenge the conventional perspective on abilities. One popular theory of intelligence is known as the Multiple Intelligences (MI). This paper emphasizes the scarcity of appropriate assessments for MI, drawing attention to Gardner’s acknowledgment of this limitation. It is contended that MI has neither gained sufficient empirical or scientific support nor provided trustworthy, reliable, and valid measures to assess its core intelligences. The study adds to the current discussion over intelligence conceptualization in educational contexts by revealing both neurological findings and neuromyth inaccuracies. It also sheds light on the limitations and possible directions for the theory’s implementation in higher education, through a critique of the restricted testing procedures available for MI. The paper also explores the limited availability of tests designed to assess each intelligence independently or as a collective set of multiple intelligences. Furthermore, it addresses the potential impact of MI on students’ academic achievement and merit, which significantly influences educational policy reforms, seeking to foster more inclusive and equitable learning environments.