Recently, through the use of parameterized fuzzy approximators, various adaptive fuzzy control schemes have been developed to deal with nonlinear systems whose dynamics are poorly understood. An important class of parameterized fuzzy approximators is constructed using radial basis function (RBF) as a membership function. However, some tuneable parameters in RBF appear nonlinearly and the determ...

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...

This paper describes the design of an adaptive direct control scheme for a class of nonlinear systems. The architecture is based on a fuzzy inference system (FIS) of Takagi Sugeno (TS) type to approximate a feedback linearization control law. The parameters of the consequent part of the fuzzy system are adapted and changed according to a law derived using Lyapunov stability theory. The asymptot...

Universal approximation is the basis of theoretical research and practical applications of fuzzy systems. Studies on the universal approximation capability of fuzzy systems have achieved great progress in recent years. In this paper, linear Takagi–Sugeno (T–S) fuzzy systems that use linear functions of input variables as rule consequent and their special case named simplified fuzzy systems that...

this paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. the uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. the contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...

In this work, we show that single input single output (SISO) fuzzy inference systems based on Fuzzy Relational Inference (FRI) with implicative interpretation of the rule base are universal approximators under suitable choice of operations for the other components of the fuzzy system. The presented proofs make no assumption on the form or representations of the considered fuzzy implications and...

A novel fuzzy reasoning method, called FSI (fuzzy similarity inference) is investigated in this paper. Firstly, the unified forms of FSI which the diverse implication operators can be employed are proposed. And the computational formulas for both fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT) are obtained. Secondly, the unified forms of α-FSI method are established, and the formulas for...

Fuzzy Rule Base Systems (FRBS) has been shown to be an important tool for problems where, due to the complexity or the imprecision, classical tools are unsuccessful. In [3,14] it has been proved that FRBS are universal approximators in the sense that for any continuous system it is possible to find a set of fuzzy rules able of approximating it with arbitrary accuracy. Now, the question is: How ...

Fuzzy logic is shown to be a very promising mathematical approach for modelling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation planning problems. Emphasis is put on the imp...

1 This work is partially supported by the FCT Portuguese Foundation for Science and Technology under project POSI/SRI/47188/2002, and by Fundação ORIENTE – Portuguese Orient Foundation ABSTRACT: Fuzzy Causal Rule Bases (FCRb) are widely used and are the most important rule bases in Rule Based Fuzzy Cognitive Maps (RB-FCM) [1][4][5][6]. However, FCRb are subject to several restrictions that crea...