Soft Computing
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"Soft Computing" covers fuzzy logic, neural networks, and evolutionary algorithms in a complete introduction to the area. The book begins by charting computing from traditional AI to computational intelligence, preparing readers for soft computing. It teaches machine learning fundamentals. The book covers fuzzy logic, including fuzzy sets, relations, and inference systems. Readers learn about fuzzy logic in expert systems and decision-making through thorough discussions on membership functions and fuzzy reasoning. In the second section, the book covers neural network evolution, construction, a...
"Soft Computing" covers fuzzy logic, neural networks, and evolutionary algorithms in a complete introduction to the area. The book begins by charting computing from traditional AI to computational intelligence, preparing readers for soft computing. It teaches machine learning fundamentals. The book covers fuzzy logic, including fuzzy sets, relations, and inference systems. Readers learn about fuzzy logic in expert systems and decision-making through thorough discussions on membership functions and fuzzy reasoning. In the second section, the book covers neural network evolution, construction, and training techniques. Readers learn about neural network models and machine learning applications, from feedforward networks to adaptive resonance structures. Genetic algorithms, their biological origin, optimization methods, and comparison to classical algorithms are covered in the book's conclusion. Reading examples and case studies helps readers understand genetic algorithms and their applicability in difficult optimization issues. "Soft Computing" is a useful reference for students, academics, and practitioners investigating the wide field of soft computing.
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