Adaptive Fuzzy Logic Systems trained by genetic evolution of their parameters are presented in this work. This technique is based on the aggregation of parameter perturbations. Neither the evaluation function nor the membership functions, have to be differentiable as required in most optimization techniques. In the classical Genetic Algorithms, the solution space of each parameter should be spe...

The control of nonlinear systems is difficult because no systematic mathematical tools exist to help find necessary and sufficient conditions to guarantee their stability and performance. The problem becomes yet more complex if some of the plant parameters are unknown. By using a Takagi-Sugeno-Kang (TSK) fuzzy plant model [1]-[4], a nonlinear system can be expressed as a weighted sum of some si...

This paper presents a new hybrid methodology for learning Sugeno-type fuzzy models via subtractive clustering, Adaptive Boosting Regression (AdaBoostR) and Unscented Kalman Filter (UKF). The generated fuzzy models are used for modeling nonlinear benchmark processes. In the proposed procedure, first one fuzzy rule is generated by subtractive clustering algorithm from available data of a given no...

Fuzzy logic programming is a growing declarative paradigm aiming to integrate fuzzy logic into logic programming. One of the most difficult tasks when specifying a fuzzy logic program is determining the right weights for each rule, as well as the most appropriate fuzzy connectives and operators. In this paper, we introduce a symbolic extension of fuzzy logic programs in which some of these para...

The aim of this study is to define a fuzzy based prediction system, that will accept the patient basic information as well as the symptoms as input and identify the chances of heart disease. The fuzzy based system is defined on multiple parameters based on which the patient disease analysis can be performed. The parameters taken in this work are Patient Age, Blood Pressure, Cholesterol Level an...

: In real life situations, especially for new products, the probability is not known due to lack of historical data and adequate information. Then these parameters and variables are treated as fuzzy parameters. Fuzzy set theory is now applied to problems in engineering, business, medical and related health sciences and natural sciences. Over the years there have been successful applications and...

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization (PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to o...

In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, L...

In this paper, we extend TOPSIS (Technique for Order Preference by Similarity Ideal Solution) for solving Large Scale Multiple Objective Programming problems involving fuzzy parameters. These fuzzy parameters are characterized as fuzzy numbers. For such problems, the α-Pareto optimality is introduced by extending the ordinary Pareto optimality on the basis of the α-Level sets of fuzzy numbers. ...

Fuzzy systems can be used for function approximation based on a set of linguistic rules. We present a method to obtain the necessary parameters for such a fuzzy system by a neuro-fuzzy training method. The learning algorithm is able to determine the structure and the parameters of a fuzzy system from sample data. The approach is an extension to our already published NE-FCON and NEFCLASS models ...