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Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control
Angielski
392 b
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The editors describe in this book, new methods for evolutionary design of intelligent systems using soft computing and their applications in modeling, simulation and control. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and evolutionary algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in four main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of evolutionary design of fuzzy systems in intelligent control, which consists of papers that propose new methods for designing and optimizing intelligent controllers for different applications. The second part contains papers with the main theme of evolutionary design of intelligent systems for pattern recognition applications, which are basically papers using evolutionary algorithms for optimizing modular neural networks with fuzzy systems for response integration, for achieving pattern recognition in different applications. The third part contains papers with the themes of models for learning and social simulation, which are papers that apply intelligent systems to the problems of designing learning objects and social agents. The fourth part contains papers that deal with intelligent systems in robotics applications and hardware implementations.
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