This book describes recent advances on hybrid intelligent
systems using soft computing techniques for diverse areas of
application, such as intelligent control and robotics, pattern
recognition, time series prediction and optimization complex
problems. Soft Computing (SC) consists of several intelligent
computing paradigms, including fuzzy logic, neural networks and
bio-inspired optimization algorithms, which can be used to produce
powerful hybrid intelligent systems. The book is organized in five
main parts, which contain a group of papers around a similar
subject. The first part consists of papers with the main theme of
type-2 fuzzy logic, which basically consists of papers that propose
new models and applications for type-2 fuzzy systems. The second
part contains papers with the main theme of bio-inspired
optimization algorithms, which are basically papers using
nature-inspired techniques to achieve optimization of complex
optimization problems in diverse areas of application. The third
part contains papers that deal with new models and applications of
neural networks in real world problems. The fourth part contains
papers with the theme of intelligent optimization methods, which
basically consider the proposal of new methods of optimization to
solve complex real world optimization problems. The fifth part
contains papers with the theme of evolutionary methods and
intelligent computing, which are papers considering soft computing
methods for applications related to diverse areas, such as natural
language processing, recommending systems and optimization.
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