Learning and Adaptation in Fuzzy Control

Soft computing is a partnership of methodologies that imitate the human mind in its ability to deal with incomplete knowledge and partial truth. It provides an ideal tool for the design of intelligent systems which exploit the tolerance for imprecision inherent in many real world problems. Fuzzy control provides a mean to model the relationship between input information and control output in an intuitive, linguistic fashion that does not depend on a precise mathematical model of the...
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Soft computing is a partnership of methodologies that imitate the human mind in its ability to deal with incomplete knowledge and partial truth. It provides an ideal tool for the design of intelligent systems which exploit the tolerance for imprecision inherent in many real world problems. Fuzzy control provides a mean to model the relationship between input information and control output in an intuitive, linguistic fashion that does not depend on a precise mathematical model of the underlying process. Combining fuzzy control with adaptive systems, neural networks and evolutionary algorithms results in powerful hybrid methods that demonstrate improved autonomy, robustness, tractability, performance and cost efficiency. This book introduces the concepts, implementation and advantages of hybrid methods that benefit from the amalgamation of the individual technologies. It provides the first comprehensive description of learning and adaptation in fuzzy control systems. Both the theoretical foundations and their applications to practical problems are lined out, and a comparison of the strengths and weaknesses of different methods is given. The authors provide new perspectives to those readers familiar with the subject of fuzzy control and offer a practical guideline to those concerned with its applications to real world problems.