Okyay Kaynak – författare

This book addresses some of the challenges suffered by the well-known and robust sliding-mode control paradigm. The authors show how the fusion of fuzzy systems with sliding-mode controllers can alleviate some of these problems and promote applicability.

Fuzzy systems used as soft switches eliminate high-frequency signal oscillations and can substantially lower the noise sensitivity of sliding-mode controllers. The amount of a priori knowledge required concerning the nominal structure and parameters of a nonlinear system is also shown to be much reduced by exploiting the general function-approximation property of fuzzy systems so as to use them as identifiers.

•a review of various existing structures of sliding-mode fuzzy control;

•a guide to the fundamental mathematics of sliding-mode fuzzy controllers and their stability analysis;

•state-of-the-art procedures for the design of a sliding-mode fuzzy controller;

•a short bibliography for each chapter for readers interested in learning more on a particular subject; and

•illustrative examples and simulation results to support the main claims made in the text.

Academic researchers and graduate students interested in the control of nonlinear systems and particularly those working in sliding-mode controller design will find this book a valuable source of comparative information on existing controllers and ideas for the development of new ones.

This book constitutes the refereed proceedings of the joint International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003.

The 138 revised full papers were carefully reviewed and selected from 346 submissions. The papers are organized in topical sections on learning algorithms, support vector machine and kernel methods, statistical data analysis, pattern recognition, vision, speech recognition, robotics and control, signal processing, time-series prediction, intelligent systems, neural network hardware, cognitive science, computational neuroscience, context aware systems, complex-valued neural networks, emotion recognition, and applications in bioinformatics.