Genetic-based new fuzzy reasoning models with application to fuzzy control

Abstract

Abstract-The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown in this paper that the performance of fuzzy control systems may be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate an optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller.

A systematic approach to a self-generating fuzzy rule-table for function approximation

“…In fact, the analysis of the internal states in the design of a fuzzy system (mainly the position of the linguistic values), with no restriction on the behavior of hybrid-fuzzy optimization techniques such as neuro-fuzzy networks and fuzzy-genetic algorithms [16], [28]- [30] shows that for each input vector many rules are activated and that the overlap is greater than two. Moreover, the position and domain of the membership functions do not usually match the characteristics of the problem being solved (the membership functions are sometimes located outside the domains of the input variables).…”

“…Since the tuning and learning of the parameters of a fuzzy system can be analyzed as an optimization problem, genetic algorithms (GA's) and artificial neural networks (ANN's) offer a possibility to solve this problem [5], [8], [9], [11], [16], [26]- [29]. The analysis of the internal states in the design of the fuzzy system (mainly the position of the membership functions), with no restriction on the behavior of the GA or ANN, shows [28], [30], that for each input vector many rules are activated and the overlap is greater than two; moreover, the position and domain of the membership functions do not usually match the characteristics of the problem being solved. Most hybrid techniques presented in the bibliography concerning the problem of modeling an unknown system by a fuzzy rule-based model pay less attention to the linguistic or fuzzy interpretation of the obtained system.…”

An Evolutionary Fuzzy Hybrid System for Educational Purposes

“…To set these rules, the program offers various menus with Windows and numerical routines. The processes of fuzzification and defuzzification variables are made by the program without user interference (Park et al, 1994). Figure 1 shows the main screen to represent the problem of parking of a vehicle.…”

Online Adaptive Fuzzy Neural Identification and Control of a Class of Mimo Nonlinear Systems

“…Therefore, fuzzy logic and neural networks have been greatly adopted in model-free adaptive control of nonlinear systems (Brown and Harris, 1994;Wang, 1997). Furthermore, a few hybrid techniques were applied to adaptation of parameters in fuzzy and/or neural controllers, like sliding mode control (Chang, 2001), Bayesian probability (MacKay, 1995), genetic algorithms (Park et al, 1994), neuron-like structure (Berenji and Khedkar, 1992), hybrid pisigma network (Jin et al, 1995) and RBF neural networks (Jang et al, 1997). However, it turns out that only adjustment of parameters will not be sufficient in many cases.…”