An efficient genetic reinforcement learning algorithm for designing Fuzzy Inference System (FIS) with out any priory knowledge is proposed in this paper. Reinforcement learning using Fuzzy Q-Learning (FQL) is applied to select the consequent action values of a fuzzy inference system, in this method, the consequent value is selected from a predefined value set which is kept unchanged during lear...

the present paper aimed at developing an approach based on fuzzy inference system (fis) for measuring of knowledge sharing in the organization. in recent years there has been increasing interest in the knowledge sharing by experts and managers in the world, according to increasing importance of knowledge as the key source of competitive advantage, organizations have made serious effort to find ...

Fuzzy Logic has found popularity in the academic community and widespread use in the industry due to ever increasing complexities in systems, at the core of which lies vagueness, uncertainty and imprecision in information. The proposition of fuzzy set theory, specifically Fuzzy Inference Systems (FIS) led to an explosion of its application in diverse fields including manufacturing especially ma...

An important and difficult issue in designing a Fuzzy Inference System (FIS) is the specification of fuzzy sets, and fuzzy rules. The aim of this paper is to demonstrate how an additional qualitative information, i.e., monotonicity property, can be exploited and extended to be part of an FIS designing procedure (i.e., fuzzy sets and fuzzy rules design). In this paper, the FIS is employed as an ...

This paper explores the learning fuzzy inference systems implemented as adaptive fuzzy-neural networks. The research into application of learning techniques to fuzzy inference systems (FIS) has matured into a family of adaptive fuzzy inference systems (AFIS). In most cases, the learning FIS and AFIS families can be interpreted as a partially connected multilayer feedforward neural network with ...

Edges detection in digital images is a problem that has been solved by means of the application of different techniques from digital signal processing, also the combination of some of these techniques with Fuzzy Inference System (FIS) has been experienced. In this work a new FIS Type-2 method is implemented for the detection of edges and the results of three different techniques for the same in...

Fuzzy Inference Systems (FIS) have the advantage of relying on the properties of Fuzzy Logic to represent imperfect information so gradually, and manipulate them from a linguistic description. This exibility of representation is more signi cant for the study of complex systems. Our aims are to propose a formal approach for describing FIS as a Discrete Event System (DES), and to extend a DES in ...

A methodology for the development of a fuzzy expert system (FES) with application to earthquake prediction is presented. The idea is to reproduce the performance of a human expert in earthquake prediction. To do this, at the first step, rules provided by the human expert are used to generate a fuzzy rule base. These rules are then fed into an inference engine to produce a fuzzy inference system...