In this paper we present a new learning method for rule-based feed-forward and recurrent fuzzy systems. Recurrent fuzzy systems have hidden fuzzy variables and can approximate the temporal relation embedded in dynamic processes of unknown order. The learning method is universal i.e. it selects optimal width and position of Gaussian like membership functions and it selects a minimal set of fuzzy...

Abstract: This paper describes a novel non-linear modelling approach by online clustering, fuzzy rules and support vector machine. Structure identification is realised by an online clustering method and fuzzy support vector machines, and the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upp...

this study recommends a gis-based (geographic information systems) and multi-criteria evaluation for site selection of gas power plant in natanz city of iran. the multi-criteria decision framework integrates legal requirements and physical constraints related to environmental and economic concerns. it also builds a hierarchy model for gas power plant suitability. the methodologies used for site...

The synthesis of genetics-based machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multi-variate non-linear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to genetics-based machine learning of fuzzy controllers, called a Pittsburgh ...

To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the alpha-cuts of equivalence relations and the alpha-cu...

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the rst one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy...

Defuzzifier circuit is one of the most important parts of fuzzy logic controllers that determine the output accuracy. The Center Of Gravity method (COG) is one of the most accurate methods that so far been presented for defuzzification. In this paper, a simple algorithm is presented to generate triangular output membership functions in the Mamdani method using the multiplier/divider circuit and...

The paper presents an automated method for generating fuzzy rules and fuzzy membership functions for pattern classification from training sets of examples. Initially, fuzzy subspaces are created from the partitions formed by the minimum and maximum of individual feature values of each class. The initial membership functions are determined according to the generated fuzzy partitions. The fuzzy s...

This paper deals with the simplest fuzzy PID controllers which employ two fuzzy sets for each of the three input variables and four fuzzy sets for the output variable. Mathematical model for a fuzzy PID controller is derived by using asymmetric Γ-function type and L-function type membership functions for each input, asymmetric trapezoidal membership functions for output, algebraic product trian...