Fundamentals of Computational Intelligence (eBook)

Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another 

This book covers the three fundamental topics that form the basis of computational intelligence:  neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation.

• Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks
• Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals
• Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence
• Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems

Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

James Keller holds the University of Missouri Curators' Professorship in the Electrical and Computer Engineering and Computer Science Departments on the Columbia Campus, and is the R.L. Tatum Professor in the College of Engineering. Dr. Keller is a Life Fellow of the IEEE, a Fellow of the International Fuzzy Systems Association, and a former president of the North American Fuzzy Information Processing Society.

Derong Liu is a Professor of Electrical and Computer Engineering at the University of Illinois at Chicago, USA, and a Professor of Automation and Electrical Engineering at the University of Science and Technology Beijing, China. Dr. Liu is a Fellow of the IEEE and a Fellow of the International Neural Network Society. He has published 17 books, including Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (2012, Wiley-IEEE Press). He is the Editor-in-Chief of Artificial Intelligence Review, and he served as the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems (2010-2015).

David Fogel is the President of Natural Selection, Inc., CEO of Natural Selection Financial, Inc., a Fellow of the IEEE, and the series editor for the Wiley-IEEE Press Series on Computational Intelligence. Dr. Fogel has 30 years of experience pioneering contributions in the field of computational intelligence, and is co-inventor of the EffectCheck® sentiment analysis system. He has written several books including Evolutionary Computation: The Fossil Record (1998) and Evolutionary Computation Toward a New Philosophy of Machine Intelligence, 3rd Edition (2005), both published by the Wiley-IEEE Press.

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Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.