Fuzzy Logic and Neural Networks

Fuzzy logic can be used to reason like humans and can deal with uncertainty other than randomness. Outlier detection is a difficult task to be performed, due to uncertainty involved in it. The outlier itself is a fuzzy concept and difficult to determine in a deterministic way. fuzzy logic system is very promising, since they exactly tackle the situation associated with outliers. Fuzzy logic that addresses the seemingly conflicting goals (i) removing noise, (ii) smoothing out outliers and certain other salient feature. This paper provides a detailed fuzzy logic used for outlier detection by discussing their pros and cons. Thus this is a very helpful document for naive researchers in this field.

Fuzzy logic can be used to reason like humans and can deal with uncertainty other than randomness. Outlier detection is a difficult task to be performed, due to uncertainty involved in it. The outlier itself is a fuzzy concept and difficult to determine in a deterministic way. fuzzy logic system is very promising, since they exactly tackle the situation associated with outliers. Fuzzy logic that addresses the seemingly conflicting goals (i) removing noise, (ii) smoothing out outliers and certain other salient feature. This paper provides a detailed fuzzy logic used for outlier detection by discussing their pros and cons. Thus this is a very helpful document for naive researchers in this field.

Forced Van der Pol oscillator exhibits chaotic behaviour and instability under certain parameters and this poses a great threat to the systems where it has been applied hence, the need to develop a control method to stabilize and control chaos in a Forced Van der Pol oscillator so as to avoid damage in the controlled system and also to prevent unmodeled dynamics from being introduced in the system. Sliding Mode control makes use of the regulatory variables derived from the controlled Lyapunov function to bring the new variables to stability. The essence of using Integral Backstepping was to prevent chattering which can occur in the control input and can cause instability to the system by igniting unmodeled dynamics. Simulation was done using MATLAB and the results were provided to show the effectiveness of the proposed control method. Integral Backstepping Sliding Mode control method was effective towards stability and chaos control. It was also robust towards matched and unmatched disturbance.

Forced Van der Pol oscillator exhibits chaotic behaviour and instability under certain parameters and this poses a great threat to the systems where it has been applied hence, the need to develop a control method to stabilize and control chaos in a Forced Van der Pol oscillator so as to avoid damage in the controlled system and also to prevent unmodeled dynamics from being introduced in the system. Sliding Mode control makes use of the regulatory variables derived from the controlled Lyapunov function to bring the new variables to stability. The essence of using Integral Backstepping was to prevent chattering which can occur in the control input and can cause instability to the system by igniting unmodeled dynamics. Simulation was done using MATLAB and the results were provided to show the effectiveness of the proposed control method. Integral Backstepping Sliding Mode control method was effective towards stability and chaos control. It was also robust towards matched and unmatched disturbance.

In this paper we present an efficient and accurate method to aggregate a set of Deep Convolutional Neural Network (CNN) responses, extracted from a set of image windows. CNN features are usually computed on the whole frame or with a dense multi scale approach. There is evidence that using multiple windows yields a better image representation nonetheless it is still not clear how windows should be sampled and how CNN responses should be aggregated. Instead of sampling the image densely in scale and space we show that selecting a few hundred windows is enough to obtain an effective image signature. We show how to use Fisher Vectors and PCA to obtain a short and highly descriptive signature that can be used effectively for image retrieval. We test our method on two relevant computer vision tasks: image retrieval and image tagging. We report state-of-the art results for both tasks on three standard datasets.

Nowadays, because of advancements in technology and increasing use of machine for daily tasks, the optimal use of these devices has become an important issue. Quadrotor is one of tools that are widely used today in various fields of military, agriculture, meteorology, researches, transportation and so on. In this paper, our model is a quadrotor. One of the issues that threaten the optimal performance of today's machines is fault. The fault is any undesired factor that disables the system's performance. The fault can be applied to different parts of an industrial system, including system actuators, sensors or parameters. In this paper, we intend to investigate the sensor fault in a quadrotor model. So that, first the sensor fault is detected and then the fault tolerant control (FTC) algorithm is designed to modify the control signals and eliminate the effect of this fault. The method presented in this paper is an analytical method named analytical redundancy relation (ARR) that can easily detect the fault occurrence and design the fault tolerant control algorithm by using residual signals. Another advantage of this method is its robustness against disturbance and uncertainty in system parameters.

In order to maintain the performance of the control systems affected by the fault, a fault-tolerant control (FTC) method is used. This controller tries to minimize the effect of the fault by modifying or correcting the control law of the system at the time of fault occurrence, so that the system can continue its normal operation. In this paper, we present an analytical method for controlling the actuator faults in quadrotor. In this method, called analytical redundancy relations, we try to estimate the system states using the analytic relations, and therefore, we do not need an observer to estimate system states. So we can achieve the faulty system states without using the observer, and we can reconfigure or modify the control laws using these estimated states. This approach is an active FTC that can handle any kind of unforeseen faults that may affect the system. In this research, the control law of the main system in the normal operation is the sliding mode control that has a good performance in dealing with nonlinear complex dynamical system models.

The integrated circuits (ICs) industry uses a number of technology computer aided design (TCAD) software tools to simulate the manufacturing and the operation of many ICs at different levels. At very low level, the simulation tools are used to simulate the device fabrication and design. These simulation tools are based on solving mathematical equations that describe the physics of doping diffusion, silicon oxidation, etching, deposition, lithography, implantation, and metallization. The simulation of physical etching solves etching equations to calculate the etching rate. And this rate is used to move the geometry of the device. The simulation of non-physical (geometrical) etching is based on geometrical Boolean operations. In this paper, we are proposing new and advanced geometrical etching algorithms for the process simulation of three dimensional (3D) micro electro mechanical systems (MEMS) and MEMS-tunable vertical cavity semiconductor optical amplifiers (VCSOAs). These algorithms are based on advanced domain decomposition methods, Delaunay meshing algorithms, and surface re-meshing and smoothing techniques. These algorithms are simple, robust, and significantly reduce the overall run time of the process simulation of 3D MEMS and MEMS-tunable laser devices. The description of the proposed etching algorithms will be presented. Numerical simulation results showing the performances of these algorithms will be given and analyzed for realistic 3D MEMS and MEMS-tunable laser devices.

Everyone be aware of COVID-19. Around .035% on the people died because of this disease. So many Researchers are trying to make Corona free World. All the Health Organisation and Departments are put their hard work for finding the solutions. In that, testing of finding the disease is costlier and also it consumes time too. Based on the disease seriousness and time, cost concern a new Screening Plan is implemented. The method of finding the infected people are so costlier and the time also prolonged. In order to minimise the cost and time the screening is needed to find out the affected people. A sampling plan not only used in Industries, the past and recent developments is to be made in the field of Genetic Algorithm, Genome Study, Bio-Statistics etc., so sampling plan is important aspect in all cases. Based on the Sampling Plan and the seriousness goes around the world, a new sampling plan is used for how to select a sample from the population and the design, construction and operating procedure also given.

The main purpose of this article is to present and explore potential applications in marketing administration related to pricingstrategyusingfuzzylogic. Considering the new trends in consumer behavior in Brazil's economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

This paper presents results on the output regulation of a single-input multi-output (SIMO) rotationaltranslational actuator (RTAC) system. The results focus primarily on stability and robustness, which are studied in light of the presence of externally generated exogenous input signals. Two exosystem types were investigated and tested. Obtained results answers the question of asymptotic stabilization and tracking of a desired trajectory in the presence of a dynamic exosystem. The results confirmed the working theory of robust stabilization using output feedback techniques, borne out of differential-geometric observer design principles. The utilized design showed good stability results which compares favourably with existing works on RTAC stabilization.

Fuzzy logic is well suited to the control of non-linear, time varying and /or any system for which it is difficult to obtain an exact mathematical model. To design a good fuzzy controller, through understanding of the desired process behavior is required. Many papers have designed fuzzy algorithm by using the deviation from the set point and its rate of change. In this research the variables can be replaced to a change in rate change in error term (CCE) and the effect of acceleration on the system performance has been considered. The quantitative and qualitative study of Acceleration Fuzzy Logic Controller (AFLC) is made. The result showed there is an efficient method for speed control of a separately excited D.C motor by using this new controller.

This paper aims at showing the application of neural network predictive control (NNPC) to counter-current heat exchangers (HEs) in series for water savings. The controlled process unit is composed of five counter-current shell-and-tube heat exchangers in series in which petroleum, coming from a distillation unit in a refinery is cooled. Neural network predictive control (NNPC) of the HEs for water savings was studied by simulations. The neural network (NN) plant model of the heat exchangers in series was obtained off-line. The two-layer network with sigmoid transfer functions in the hidden layer and linear transfer functions in the output layer was trained using the Levenberg-Marquardt (LM) algorithm. The neural network predictive control (NNPC) combines the advantages of neural-network-based modelling and model-based predictive control (MPC). Neural-network modelling is suitable for modelling non-linear processes, processes with asymmetric dynamics and processes with uncertainty. M...

This paper presents an evaluation of performances of rice cooking system using Intelligent Controller that is Fuzzy Logic Controller (FLC) to meet the special requirements and some limitations of the rice cooking system. A new inference scheme is given to estimate the amount of rice and water to be used, and the temperature will be controlled according to the amount of rice and the time while cooking. The FLC system is designed by using MATLAB Fuzzy Logic Toolbox. The results obtained from the simulation software is given in this paper. This study proposes an automatic rice cooker temperature controller, which uses the difference between the amount of time and quantity of rice inside the cooker to determine the required temperature. The difference is assumed to affect the heating element in order to achieve the desired set point. The operating voltage is also taken into account. The research involves finding the mathematical model of a rice cooking system, designing a controller and...

Resumo: Este artigo apresenta uma arquitetura diferente de Sistema Tutor Inteligente, onde se inclui o docente como agente externo, organizador e interventor do processo de atendimento do aluno no contexto do sistema. O arcabouco conceitual usado para apoiar esta proposta se fundamenta na Teoria da Aprendizagem Significativa de Ausubel e no uso de Mapas Conceituais como elementos para se organizar o conteudo do dominio da aplicacao. Na modelagem do aluno e incorporada a Logica Fuzzy que permite representar informacoes do aluno associadas aos aspectos mais qualitativos e incertos. Abstract: This paper presents a proposal to build an Intelligent Tutoring System using Fuzzy Logic to model the student in order to handle with qualitative aspects regarding his/her behavior. The domain module is modeled based on Conceptual Maps that allow teacher to better organize the contents in a hierarchical way. In this proposal the teacher works as an external agent which can interfere to modify or a...

This paper presents results on the output regulation of a single-input multi-output (SIMO) rotationaltranslational actuator (RTAC) system. The results focus primarily on stability and robustness, which are studied in light of the presence of externally generated exogenous input signals. Two exosystem types were investigated and tested. Obtained results answers the question of asymptotic stabilization and tracking of a desired trajectory in the presence of a dynamic exosystem. The results confirmed the working theory of robust stabilization using output feedback techniques, borne out of differential-geometric observer design principles. The utilized design showed good stability results which compares favourably with existing works on RTAC stabilization.

The main purpose of this article is to present and explore potential applications in marketing administration related to pricing strategy using fuzzy logic. Considering the new trends in consumer behavior in Brazil's economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.

The main purpose of this article is to present and explore potential applications in marketing administration related to pricing strategy using fuzzy logic. Considering the new trends in consumer behavior in Brazil's economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.

A new idea presented in this paper is implementation of the neural network (NN) predictive controllers in the complex control structures that are used in industrial applications. The conventional feedback PID control, simple neural network predictive control (NNPC) and two complex control structures with NN predictive controllers were studied using simulation experiments and compared. The NN predictive controllers in the role of the primary controllers were used in the cascade control and in the control system with the auxiliary control input. All mentioned control structures were used for control of five counter-current heat exchangers in series that were used for cooling of a product of distillation. The neural network (NN) plant model of the heat exchangers was obtained off-line. The simulation experiments showed that the NNPC-based control system with the main NN predictive controller and with the auxiliary control input significantly reduced both, the settling time and the over...

This paper aims at showing the application of neural network predictive control (NNPC) to counter-current heat exchangers (HEs) in series for water savings. The controlled process unit is composed of five counter-current shell-and-tube heat exchangers in series in which petroleum, coming from a distillation unit in a refinery is cooled. Neural network predictive control (NNPC) of the HEs for water savings was studied by simulations. The neural network (NN) plant model of the heat exchangers in series was obtained off-line. The two-layer network with sigmoid transfer functions in the hidden layer and linear transfer functions in the output layer was trained using the Levenberg-Marquardt (LM) algorithm. The neural network predictive control (NNPC) combines the advantages of neural-network-based modelling and model-based predictive control (MPC). Neural-network modelling is suitable for modelling non-linear processes, processes with asymmetric dynamics and processes with uncertainty. M...

The paper is devoted to advanced control of a tubular heat exchanger with focus to energy savings. The controlled tubular heat exchanger (HE) was used for petroleum pre-heating by hot water. The controlled output was the measured temperature of the petroleum in the output stream and the control input was the volumetric flow rate of hot water. Two advanced control strategies were investigated in the set-point tracking, the neural-networkbased predictive control and the robust model-based predictive control with integral action and with soft constraints on control inputs. The advanced control of the heat exchanger was implemented in the MATLAB/Simulink simulation environment. Simulation results obtained using advanced controllers were compared with the results ensured by a conventional PID controller and they confirmed significant improvement of the control performance. Moreover, advanced controllers reduced energy consumption measured by the total consumption of hot fluid used for he...

Advanced process control includes optimization-based tools that are recently widely implemented in industry to maximize economical effectiveness and to minimize environmental impact. Robust model predictive control (MPC) is one of these strategies and it combines benefits of model predictive control and robust control approaches. This study investigates improvement of control performance and increase of energy savings using the soft-constrained robust MPC with integral action for a laboratory plate heat exchanger. Soft constraints on control inputs keep the heat exchanger in required operation conditions and enable to use the feasible range of manipulated variable effectively with decreasing of energy cost. Integral action of the predictive controller ensures offset-free reference tracking. Simulation results obtained using the newly designed robust predictive controller with soft constraints and integral action confirm improved control response and increased energy savings in compa...

This paper presents results on the output regulation of a single-input multi-output (SIMO) rotationaltranslational actuator (RTAC) system. The results focus primarily on stability and robustness, which are studied in light of the presence of externally generated exogenous input signals. Two exosystem types were investigated and tested. Obtained results answers the question of asymptotic stabilization and tracking of a desired trajectory in the presence of a dynamic exosystem. The results confirmed the working theory of robust stabilization using output feedback techniques, borne out of differential-geometric observer design principles. The utilized design showed good stability results which compares favourably with existing works on RTAC stabilization.

The main purpose of this article is to present and explore potential applications in marketing administration related to pricingstrategyusingfuzzylogic. Considering the new trends in consumer behavior in Brazil's economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.

1 B.E. Student, Electrical Engineering Department, Itm Universe Vadodara 2 B.E. Student, Electrical Engineering Department, Itm Universe Vadodara 3 B.E. Student, Electrical Engineering Department, Itm Universe Vadodara 4 Assistant Professor, Electrical Engineering Department, Itm Universe Vadodara __________________________________________________________________________________________ Abstract: The Photovoltaic (PV) module is the interface which converts light into electricity. This paper includes mathematical and simulation model for PV cells. The model is able to simulate both the P-V & I-V characteristics curves. One of the main challenge is harvesting power from a PV source is maximum power point tracking (MPPT). It is due to non linear behavior & characteristics of PV array. Proposed system consists of high gain boost converter feeding three level diode clamped inverter. 60W Solarex MSX60 PV panel is chosen for evaluating the developed model.

The paper presents an advanced control strategy that uses the neural network predictive controller and the fuzzy controller in the complex control structure with an auxiliary control variable. The controlled tubular heat exchanger was used for pre-heating of petroleum by hot water. The heat exchanger was modelled as a nonlinear system with interval parametric uncertainty. The set point tracking and the disturbance rejection using intelligent control strategies were investigated. The control objective was to keep the outlet temperature of the pre-heated petroleum at a reference value. Simulations of control of the tubular heat exchanger were done in the Matlab/Simulink environment. The neural network predictive control (NNPC) with fuzzy controller was compared with classical PID control. Simulation results obtained using NNPC with fuzzy controller and those obtained by classical PID control confirmed the effectiveness and superiority of the presented advanced control approach.

In this paper, a fault-tolerant control system based on back-stepping integral sliding mode controller (BISMC) is designed and analyzed for both nonlinear translational and rotational subsystems of the quadrotor unmanned aerial vehicles (UAVs). The novelty of this paper is about combination of a classic controller with a repetitive algorithm to reduce the response time to actuator faults and have better tracking performance. The actuator fault is defined based on the loss of effectiveness and bias fault. Next, the iterative learning control algorithm (ILCA) is used to compensate for the unknown fault input according to previous recorded experiences. In the normal condition (without actuators fault), BISMC can force the actual trajectories toward the desired commands and reduce chattering about control signals, and in the presence of the actuators fault or external disturbances, the mentioned learning algorithm can incline the accuracy of the tracking performance and compensate for t...

This paper presents results on the output regulation of a single-input multi-output (SIMO) rotationaltranslational actuator (RTAC) system. The results focus primarily on stability and robustness, which are studied in light of the presence of externally generated exogenous input signals. Two exosystem types were investigated and tested. Obtained results answers the question of asymptotic stabilization and tracking of a desired trajectory in the presence of a dynamic exosystem. The results confirmed the working theory of robust stabilization using output feedback techniques, borne out of differential-geometric observer design principles. The utilized design showed good stability results which compares favourably with existing works on RTAC stabilization.

This paper presents detailed modelling of a grid-connected photovoltaic (PV) system components. The study is helpful to understand the working principles of the PV system. The performance of the system has been discussed by means of a Matlab/Simulink Toolbox. The results show that the PV system capable of tracking the maximum power point (MPP) quickly and precisely in case of sudden changes in solar radiation, cell temperature, and in case of existence of sand. Under the non-uniform atmospheric conditions, to get a high performance of the PV system, appropriate converters are required to operate at the MPP.

Optimal operation of integrated heat exchangers is a challenge task in the field of process control due to system nonlinearities, disturbances and adequate model identification. This paper describes the design of an advanced neural network predictive control (NNPC) applied to a heat exchanger network. A case study with two hot and one cold streams, through three counter-current heat exchangers is used to test the proposed strategy. A lumped dynamic model is built based on the concept of multi-cells topology (mixed tanks), where the hot and cold cells are connected by a wall element throughout the heat exchanger length. Each cell is assumed perfectly mixed and all physical properties are constant. A distributed behavior is achieved by increasing the number of cells. The main assumptions of the lumped model are constant temperature in each cell, heat exchanger volume and area equally distributed between cells and negligible heat loss to the environment. The predictive controller relies on a neural-based model of the plant that is used to identify the system and to predict future performance over a predefined horizon. Results showed good control output regarding set point tracking.

Optimal operation of integrated heat exchangers is a challenging task in the field of process control due to system nonlinearities, disturbances and adequate model identification. This paper describes the design of an advanced neural network predictive control (NNPC) applied to a heat exchanger network. A case study with two hot and one cold streams, through three counter-current heat exchangers is used to test the proposed strategy. A lumped dynamic model is built based on the concept of multi-cells topology (mixed tanks), where the hot and cold cells are connected by a wall element throughout the heat exchanger length. Each cell is assumed perfectly mixed and all physical properties are constant. A distributed behavior is achieved by increasing the number of cells. The main assumptions of the lumped model are constant temperature in each cell, heat exchanger volume and area equally distributed between cells and negligible heat loss to the environment. The predictive controller rel...

This paper describes comparative study of various controllers on Rotary Inverted Pendulum (RIP). PID, LQR, FUZZY LOGIC and H∞ controllers are tried on RIP in MatLab Simulink. The same four controllers have been tested on test bed of RIP system the controllers are compared from various aspects. The controllers in simulink are compared with the controllers in real time.

The main purpose of this article is to present and explore potential applications in marketing administration related to pricing strategy using fuzzy logic. Considering the new trends in consumer behavior in Brazil's economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.

Outlier detection is an important task in a wide variety of application areas. In this paper, a proposed method based on fuzzy clustering approaches for outlier detection is presented. We first perform the c-means fuzzy clustering algorithm. Small clusters are then determined and considered as outlier clusters. The rest of outliers (if any) are then detected in the remaining clusters based on temporary removing a point from the data set and recalculating the objective function. If a noticeable change occurred in the Objective Function (OF), the point is considered an outlier. Experimental results show that our method works well. The test results show that the proposed approach gave good results when applied to different data sets.

This paper presents results on the output regulation of a single-input multi-output (SIMO) rotationaltranslational actuator (RTAC) system. The results focus primarily on stability and robustness, which are studied in light of the presence of externally generated exogenous input signals. Two exosystem types were investigated and tested. Obtained results answers the question of asymptotic stabilization and tracking of a desired trajectory in the presence of a dynamic exosystem. The results confirmed the working theory of robust stabilization using output feedback techniques, borne out of differential-geometric observer design principles. The utilized design showed good stability results which compares favourably with existing works on RTAC stabilization.

The work proposed in this paper aims to design a robust stabilization of an underactuated quadrotor UAV system in presence of sensor failures. The dynamical model of quadrotor while taking into account various physical phenomena, which can influence the dynamics of a flying structure is presented. Subsequently, a new control strategy based on backstepping approach is developed. Lyapunov based stability analysis shows that the proposed control strategy design keep the stability of the closed loop dynamics of the quadrotor UAV even after the presence of sensor failures. Numerical simulation results are provided to show the good tracking performance of proposed control laws.

Optimal operation of integrated heat exchangers is a challenge task in the field of process control due to system nonlinearities, disturbances and adequate model identification. This paper describes the design of an advanced neural network predictive control (NNPC) applied to a heat exchanger network. A case study with two hot and one cold streams, through three counter-current heat exchangers is used to test the proposed strategy. A lumped dynamic model is built based on the concept of multi-cells topology (mixed tanks), where the hot and cold cells are connected by a wall element throughout the heat exchanger length. Each cell is assumed perfectly mixed and all physical properties are constant. A distributed behavior is achieved by increasing the number of cells. The main assumptions of the lumped model are constant temperature in each cell, heat exchanger volume and area equally distributed between cells and negligible heat loss to the environment. The predictive controller relies on a neural-based model of the plant that is used to identify the system and to predict future performance over a predefined horizon. Results showed good control output regarding set point tracking.

A parallel restoration procedure obtained through a splitting of the signal into multiple signals by the paired transform is described. The set of frequency-points is divided by disjoint subsets, and on each of these subsets, the linear filtration is performed separately. The method of optimal Wiener filtration of the noisy signal is considered. In such splitting, the optimal filter is defined as a set of sub filters applied on the splitting-signals. Two new models of filtration are described. In the first model, the traditional filtration is reduced to the processing separately the splitting-signals by the shifted discrete Fourier transforms (DFTs). In the second model, the not shifted DFTs are used over the splitting-signals and sub filters are applied. Such simplified model for splitting the filtration allows for saving 2ܰ − ‫ݎ(4‬ + 1) operations of complex multiplication, for the signals of length ܰ = ‫,ݎ^2‬ ‫ݎ‬ > 2. .

As the Photovoltaic System uses the solar energy as one of the renewable energies for the electrical energy production has an enormous potential. The PV system is developing very rapidly as compared to its counterparts of the renewable energies. The DC voltage generated by the PV system is boosted by the DC-DC Boost converter. The utility grid is incorporated with the PV Solar Power Generator through the 3-ı PWM DC-AC inverter, whose control is provided by a constant current controller. This controller uses a 3-ı phase locked loop (PLL) for tracking the phase angle of the utility grid and reacts fast enough to the changes in load or grid connection states , as a result, it seems to be efficient in supplying to load the constant voltage without phase jump. An artificial neuron is a device with many inputs and one output. By using artificial neuron networks, the control algorithm implemented in the boost converter enhances by reducing the response time and hence, transient response for this converter is improved.

It has never been accomplished to describe our behavior mathematically. Due to the fact that human behavior is highly erratic even the understanding of its causes are still sketchy. Assuming that we are all equal in our regulation of thought and behavior there are simply too many differences and partially inconsistencies, the attempts stopped in its onset. For having defined the five major groups of mankind, the sociopath, the artist, the median, the, and the psychopath［1］each group is related to each other but his decision making is hardwired differently and thou probably more easy to grasp than sticking to the consistency of all appearance for the regulation of behavior is quite similar in the five groups but different in its limits. Using the more formalistic tools of mathematics, this can open the review of the equations in verifying or falsifying the predictions on future behavior in an individual, at least after defining the group affiliation. Therefore a self-test has been established［2］to predetermine the group. In a hybrid-species ［3］, the eight main neuro-receptors in each group to have two optional origins. Measuring each by its own dominating patterns not only the amalgamation in each group can be defined but also the native patterns of the non-hybrid ancestry ［4］. Not only the variance of possible combination can distribute to the limits of brain-equations but also the time-axis of our memory, being rather different, illuminating the highly different decision making among offspring of hunters and farmers. A phenomenon probably explaining the variance in processing memory by peripheral distributed groups of ADHD and autism for ADHD memorizes in combining data with importance and such is been given an emotional response to the recall, while autism is mainly been given the exact time reference stored in a continuous frame of time-preference. The latter therefore have problems to distinguish between important and not important and the former lacks a passing timeframe, mirroring the primary form of acquiring resource, farming or hunting. With the boundaries set the graphs of the equation on resource-projection looks highly different by only changing its limits. Having not only focussed on the designation of human groups but also in behavioral shifts over time (from social to non-social) and the exact denomination of similar behavior, some rather simple equation could be defined to predict and consequently proof the predicate. Not only the proper use of words is necessary but also awareness that non-social individuals will often not answer truthfully. A topic that also can be mathematically predicted. The outcome will revolutionize our perception of mankind and ourselves, dawning more than one academic discipline, probably enabling us to go virtual and back again.

This paper presents results on the output regulation of a single-input multi-output (SIMO) rotational-translational actuator (RTAC) system. The results focus primarily on stability and robustness, which are studied in light of the presence of externally generated exogenous input signals. Two exosystem types were investigated and tested. Obtained results answers the question of asymptotic stabilization and tracking of a desired trajectory in the presence of a dynamic exosystem. The results confirmed the working theory of robust stabilization using output feedback techniques, borne out of differential-geometric observer design principles. The utilized design showed good stability results which compares favourably with existing works on RTAC stabilization.

The multilevel multi-string inverter has gained much attention in recent years due to its advantages in lower switching loss, better electromagnetic compatibility, higher voltage capability, and lower harmonic distortion. Solar Energy is one of the favorable renewable energy resources and the multilevel inverter has been proven to be one of the important enabling technologies in photovoltaic (PV) utilization. As the number of levels increases, it is important to control more switches in parallel with their concurrent processing capability. This paper proposes an IGBT clamped three phase nine levels photovoltaic inverter topology with a multicarrier dual reference pulse-width modulated (PWM) control scheme. Four carrier waves of the fundamental frequency and different amplitudes are compared with two references (a sine wave and its opposite) for generating the control signals of the switches. Some DC/DC boost converters are used to amplify the voltage produced by the photovoltaic generators. Each of these converters is controlled by an MPPT algorithm in order to track the maximum power point of the GPV. Results of simulation in Matlab environment are given and discussed.

Fuzzy logic is highly appropriate and valid basis for developing knowledge-based systems in medicine for different tasks and it has been known to produce highly accurate results. Examples of such tasks include syndrome differentiation, likelihood survival for sickle cell anaemia among paediatric patients, diagnosis and optimal selection of medical treatments and real time monitoring of patients. For this paper, a Fuzzy logic-based system is untaken used to provide a comprehensive simulation of a prediction model for determining the likelihood of detecting Chronic Kidney failure/diseases in humans. The Fuzzy-based system uses a 4-tuple record comprising of the following test taken: Blood Urea Test, Urea Clearance Test, Creatinine Clearance test and Estimated Glomerular Filtrate rate(eGFR).Understanding of the test was elicited from a private hospital in Ibadan through the help of an experienced and qualified nurse which also follows same test according to National Kidney Foundation. This knowledge was then used in the developing the simulated and rule-base prediction model using MATLAB software. The paper also follows the 3 major stages of Fuzzy logic. The results of fuzzification of variables, inference, model testing and defuzzification of variables was also presented. This in turn simplifies the complication involved in detecting Chronic Kidney failure/disease using Fuzzy logic based model.

Chronic Kidney Disease (CKD) is usually characterized by a gradual loss of the functioning which the kidney does over time due to various factors. Early prediction and treatment save the kidney and halts the progress of CKD. CKD disease is being viewed as global public health issue for the past decade. The greatest threat for this deadly disease is developing countries where getting therapy is very expensive. The importance of predicting individuals who are at risk of CKD as well as applying clustering techniques cannot be underestimated since these can modify the progression of the disease. Identifying the silent killer disease early offers best opportunities for implementing possible strategies for lessening the probability of kidney loss. Neuro-fuzzy algorithm is applied to determine the risk of CKD in patients. Predictions done using neuro-fuzzy gave an accuracy of 97 percent. Using selected features, prediction for CKD disease is done so as to identify the risk. The results of the prediction are clustered to identify the percentage of patients with a high risk of having kidney disease who have a higher probability of being diabetic. Using hierarchical clustering three clusters formed show that there is a strong relationship between chronic kidney and diabetes.

The main purpose of this article is to present and explore potential applications in marketing administration related to pricingstrategyusingfuzzylogic. Considering the new trends in consumer behavior in Brazil's economy and the consistent growth of C and D social classes an application was developed by the authors to better understand and adjust pricing strategies: The Think Fuzzy System that combines fuzzy logic (COPPE Cosenza Model), and some other related strategic concepts, supported by mathematical microeconomic modeling, utility factor, indifference curves and an experiential hierarchic clustering model.

Synergetic Control and synchronization of two different chaotic systems is presented in this paper and simulation results are given illustrating the effectiveness of the robust control technique applied. Similar to the sliding mode approach but without damaging chattering, synergetic control is a robust approach which doesn't require linearization and should be suitable for real-time implementation for not requiring a discontinuous term in its control law.

The paper presents a method based on a fuzzy inference system that is capable of pointing out outliers in a series of data. The proposed algorithm has been adopted in order to process data coming from a real industrial context. The overall purpose of the work is to point out anomalous data due to several causes such as erroneous measurements, errors made by the operator that filled the database or anomalous process conditions. A standard statistical technique for outlier detection as been exploied for the same purpose: the performance obtained by the two methods are compared and discussed.