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Gastrointestinal parasitism is one of the diseases that has the highest economic impact on the Argentinian beef production system, rendering it inefficient. In the region of the Humid Pampas, it has been estimated that 22 million dollars... more
The advent of the World Wide Web during the last decade has brought unique challenges for organizations across the globe and inspired them to adapt to a new order of information distribution. The availability and accessibility of various... more
In this research, the researchers have managed to design a model to investigate the current trend of stock price of the "IRAN KHODRO corporation" at Tehran Stock Exchange by utilizing an Adaptive Neuro - Fuzzy Inference system.... more
This paper discusses fuzzy reasoning for approximately realizing nonlinear functions by a small number of fuzzy if-then rules with different specificity levels. Our fuzzy rule base is a mixture of general and specific rules, which overlap... more
We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems. First, we examine the search ability of each approach to... more
Many image processing applications involve a pattern classification stage. In this paper we propose a classifier based on fuzzy if-then rules that allows the incorporation of weighted training patterns which can be used to adjust the... more
Data projection is an important tool in exploratory data analysis. Sammon's non linear projection method lacks predictability and is ineffective for large data sets. To introduce predictability we implement an extension of... more
International Journal of Education, Issue May 2019, Vol. 11, UGC Approved Journal (S.No.46229) ISSN(Online):2347-4343, Web Presence: http://ijoe.vidyapublications.com © 2019 Vidya Publications. Authors are responsible for any plagiarism... more
Most methods of fuzzy rule-based system identification (SI) either ignore feature analysis or do it in a separate phase. This paper proposes a novel neuro-fuzzy system that can simultaneously do feature analysis and SI in an integrated... more
Fuzzy systems are widely used in most fields of science and engineering, mainly because the models they produce are robust, accurate, easy to evaluate and capture real-world uncertainty better than do the classical alternatives. We... more
A novel self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) is suggested in this paper, with feature selection capabilities, for the classification of an IKONOS image. The structure of the proposed network is developed in a... more
A novel Self-Organizing Neuro-Fuzzy Multilayered Classifier, the GA-SONeFMUC model, is proposed in this paper for land cover classification of multispectral images. The model is composed of generic fuzzy neuron classifiers (FNCs) arranged... more
An autonomous mobile robot (AMR) has to cope with uncertain, incomplete or approximate information. Moreover it has to identify sudden perceptual situations to manoeuvre in real time. This paper describes a fuzzy rule based system (FRBS)... more
Fuzzy rule-based systems (FRBSs) are a common alternative for applying fuzzy logic in different areas and real-world problems. The schemes and algorithms used to generate these types of systems imply that their performance can be analyzed... more
The aim of this paper is to propose a general methodology applicable to any rule based fuzzy model generated by any precise or linguistic fuzzy algorithm to improve the linguistic-accuracy trade-off. Here, the neuro-fuzzy system FasArt... more
One of the problems that focus the research in the linguistic fuzzy modeling area is the trade-off between interpretability and accuracy. To deal with this problem, different approaches can be found in the literature. Recently, a new... more
In this paper, fuzzy inference models for pattern classifications have been developed and fuzzy inference networks based on these models are proposed. Most of the existing fuzzy rule-based systems have difficulties in deriving inference... more
In this paper, fuzzy inference models for pattern classifications have been developed and fuzzy inference networks based on these models are proposed. Most of the existing fuzzy rule-based systems have difficulties in deriving inference... more
When quantitative models are used to support decision-making on complex and important topics, understanding a model's "reasoning" can increase trust in its predictions, expose hidden biases, or reduce vulnerability to adversarial attacks.... more
The cooperative rules (COR) methodology [2] is based on a combinatorial search of cooperative rules performed over a set of previously generated candidate rule consequents. It obtains accurate models preserving the highest... more
Within the field of linguistic fuzzy modeling with fuzzy rule-based systems, the automatic derivation of the knowledge base from numerical data is an important task. In this contribution, we propose a new approach to automatically learn... more
In this paper the concept of orthogonal fuzzy rule-based systems is introduced. Orthogonal rules are an extension to the de nition of orthogonal vectors when the vectors are vectors of membership functions in the antecedent part of rules.... more
Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy... more
Even though engineering and applied sciences deal with numerical data, they have successfully implemented fuzzy logic by using fuzzy rule based (FRB) systems by verbalizing data. On the other hand, social sciences such as sociology,... more
The main objective of this work is to assess the impact of the initial conditions regarding interpretability and accuracy on the optimizer's performance and present some guidelines in order to assist the designer. Automatic infusion... more
A hierarchical knowledge-based environment for linguistic modeling: models and iterative methodology
Although linguistic models are highly descriptive, they su er from inaccuracy in some complex problems. This fact is due to problems related to the in exibility of the linguistic rule structure that has been considered. Moreover, methods... more
A new fuzzy filter is presented for noise reduction of images corrupted with additive noise. The filter consists of two stages. In the first stage, all the pixels of image are processed for determining noisy pixels. For this, a fuzzy rule... more
Ripple Down Rules (RDR) is an incremental Knowledge Acquisition (KA) technique that allows experts themselves to be in charge of performing the KA as well as the maintenance of the system. Although there are various real RDR approaches,... more
Nonlinear mapping has been used in the past for data structure analysis. Many interesting heuristic approaches to map n-dimensional data to a lower dimensional space such that the local structure of the original data is reserved have been... more
Recently, Multi-Objective Evolutionary Algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of Fuzzy Rule-Based Systems. It is known that both requirements are usually contradictory,... more
In this paper the application of a multiagent system for controlling a set of traffic signals is considered. A fuzzy logic control scheme to regulate the flow of traffic approaching a set of three intersections is presented. The signal... more
In this contribution, we will analyse the importance of the fuzzy partition granularity for the linguistic variables in the design of fuzzy rule-based systems (FRBSs). In order to put this into eect, we will study the FRBS behaviour... more
Worldwide river regulation by damming and water diversion has altered natural flow regimes (Poff et al., 1997), which negatively affects all living components of these ecosystems such as riparian vegetation, macrobenthos and fishes (Poff... more
Environmental flow assessment (EFA) involving microhabitat preference models is a common approach to set ecologically friendly flow regimes in territories with ongoing or planned projects to develop river basins, such as many rivers of... more
In complex multidimensional problems with a highly nonlinear input-output relation, inconsistent or redundant rules can be found in the fuzzy model rule base, which can result in a loss of accuracy and interpretability. Moreover, the... more
In this work we propose the hybridization of two techniques to improve the cooperation among the fuzzy rules: the use of rule weights and the Cooperative Rules learning methodology. To do that, the said methodology is extended to include... more
The tuning of Fuzzy Rule-Based Systems is often applied to improve their performance as a post-processing stage once an appropriate set of fuzzy rules has been extracted. This optimization problem can become a hard one when the size of... more
Recent studies in smart homes have proposed methods to use a laser pointer for interacting with home devices, which represents a more user-friendly and less expensive home device control environment. However, detecting the laser spot on... more
Recently, Multi-Objective Evolutionary Algorithms have been also applied to improve the difficult tradeoff between interpretability and accuracy of Fuzzy Rule-Based Systems. It is know that both requirements are usually contradictory,... more
One of the problems associated to linguistic Fuzzy Modeling is its lack of accuracy when modeling some complex systems. To overcome this problem, many different possibilities of improving the accuracy of linguistic fuzzy modeling have... more
Recently, Multi-Objective Evolutionary Algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of Fuzzy Rule-Based Systems. It is known that both requirements are usually contradictory,... more
This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi-Sugeno-Kang~TSK! rules. This allow us to start from prototypes considering the interaction between input and output... more
Linguistic fuzzy modelling, developed by linguistic fuzzy rule-based systems, allows us to deal with the modelling of systems by building a linguistic model which could become interpretable by human beings. Linguistic fuzzy modelling... more
One important Artificial Intelligence tool for automatic control is the use of fuzzy logic controllers, which are fuzzy rule-based systems comprising expert knowledge in form of linguistic rules. These rules are usually constructed by an... more
This work presents the use of local fuzzy prototypes as a first approximation to obtain accurate local semantics-based Takagi-Sugeno-Kang rules. A two-stage evolutionary algorithm considering the interaction between input and output... more
In this work we propose the hybridization of two techniques to improve the cooperation among the fuzzy rules: the use of rule weights and the Cooperative Rules learning methodology. To do that, the said methodology is extended to include... more
The use of Mamdani-type fuzzy rule-based systems (FRBSs) allows us to deal with the modeling of systems building a linguistic model clearly interpretable by human beings. However, the accuracy obtained is not sometimes as good as desired.... more
Nowadays, Linguistic Modeling is considered to be one of the most important areas of application for Fuzzy Logic. Linguistic Mamdani-type Fuzzy Rule-Based Systems (FRBSs), the ones used to perform this task, provide a human-readable... more
In the last years, multi-objective genetic algorithms have been successfully applied to obtain Fuzzy Rule-Based Systems satisfying different objectives, usually different performance measures. Recently, multi-objective genetic algorithms... more