A fuzzy rule-based evidential reasoning (FURBER) approach has been proposed recently, where a fuzzy rule-base designed on the basis of a belief structure (called a belief rule base) forms a basis in the inference mechanism of FURBER. This kind of rule-base with both subjective and analytical elements may be difficult to build in particular as the system increases in complexity. In this paper, a...

Because of increasingly application of fuzzy systems, great deal of attention has been paid to design of fuzzy controller system. Many methods of fuzzy controller design have been suggested such as genetic algorithm and neural network. In this paper, our purpose is to design fuzzy controller rule base by GA. In fact, by GA through possible rule base we search for a subset of optimal rules for f...

Fuzzy technology became a very important controlling method in complex systems where traditional methods are unsuccessful. It was proved in [19] that fuzzy rule systems can be used as general approximators of any complex continuous systems. The key element of the approximation process is the construction of the corresponding fuzzy rule system that encapsulates the knowledge on the problem domai...

An adaptive-resonance theory (ART)-based fuzzy controller is presented for the adaptive navigation of a quadruped robot in cluttered environments, by incorporating the capability of ART in stable category recognition into fuzzy-logic control for selecting the adequate rule base. The environment category and the navigation mechanism are first described for the quadruped robot. The ART-based fuzz...

In this paper fuzzy rule inconsistency resolution and fuzzy rule insertion methods are proposed for fuzzy neural networks. Necessity support and possibility support (referred to as support pair) are applied to detect and remove inconsistencies. In addition to the support pair, the concept of initial learning point is used to handle rule insertion. We demonstrate the use of the proposed methods ...

A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This net...

This paper proposes two different approaches to apply Genetic Algorithms to Fuzzy Logic Controllers whose Rule Base is defined through a set of rules. The first one uses the knowledge base of the system as the population of the genetic system (a single rule containing the description of the corresponding fuzzy sets is an individual of the population), while the second uses the knowledge base (c...

The definition of the Fuzzy Rule Base is one of the most important and difficult tasks when designing Fuzzy Systems. This paper discusses the results of two different hybrid methods, previously investigated, for the automatic generation of fuzzy rules from numerical data. One of the methods, named DoC-based, proposes the creation of Fuzzy Rule Bases using genetic algorithms in association with ...

One of the common problems for fuzzy system implementation arises from the complications of the fuzzy inference process. Extra computations are required to deduce a consequence due to nature of fuzzy sets. Furthermore, considerable simulations need to be performed to verify system functions. In order to reduce the prototyping time, the fuzzy system is partitioned into hardware and software port...

For the design of a fuzzy controller it is necessary to choose, besides other parameters, suitable membership functions for the linguistic terms and to determine a rule base. This paper deals with the problem of nding a good rule base | the basis of a fuzzy controller. Consulting experts still is the usual but time{consuming and therefore rather expensive method. Besides, after having designed ...