This book is devoted to reporting innovative and significant progress in fuzzy system engineering. Given the maturation of fuzzy logic, this book is dedicated to exploring the recent breakthroughs in fuzziness and soft computing in favour of intelligent system engineering. This monograph presents novel developments of the fuzzy theory as well as interesting applications of the fuzzy logic exploiting the theory to engineer intelligent systems.
Computational system modelling is full of ambiguous situations, wherein the designer cannot decide, with precision, what should be the outcome of the system. In , L. Zadeh introduced for the first time the concept of fuzziness as opposed to crispiness in data sets. When he invented fuzzy sets together with the underlying theory, Zadeh’s main concern was to reduce system complexity and provide designer with a new computing paradigm that allow approximate results. Whenever there is uncertainty, fuzzy logic together with approximate reasoning apply. Fuzzy logic and approximate reasoning [18, 19] can be used in system modelling and control as well as data clustering and prediction , to name only few appropriate utilisations. Furthermore, they can be applied to any discipline such as finance , image processing [7, 16], temperature and pressure control [11, 22], robot control [9, 14], etc.