Performance of many physical systems is inherently nonlinear and must be described by nonlinear mathematical models. But some of these systems have unknown structure and it is not possible to provide accurate mathematical model for them. Thus, conventional control methods cannot be used to control these systems. Therefore, intelligent computational techniques such as fuzzy logic, neural networks, genetic algorithms and so on are recently applied to solve control problems of dynamic systems with unknown structure or systems with uncertainty in the structure and parameters. On the other hand, in recent years based on combination of intelligent computation and wavelet theory, new methods such as wavelet neural networks (W) and fuzzy wavelet networks (FWNs) has been proposed. Because these networks combining neural network learning ability and properties of wavelet function, not just preserve the multi resolution analysis of wavelet, but also have the advantages such as simple structure, high approximation accuracy and a good generalization capability for nonlinear systems. In this thesis, an indirect adaptive fuzzy controller for a justify; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr; VERTICAL-ALIGN: top" Key words Fuzzy wavelet networks, adaptive fuzzy control, nonlinear control, wavelet neural networks
