Learning and Soft Computing
This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
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About the Author
Vojislav Kecman is Associate Professor in the School of Engineering at Virginia Commonwealth University.
—Zoran Gajic, Department of Electrical and Computer Engineering, Rutgers University
—Vladimir Cherkassky, Department of Electrical and Computer Engineering, University of Minnesota, Twin Cities
—Jacek M. Zurada, S.T. Fife Professor of Electrical and Computer Engineering, University of Louisville, and Editor-in-Chief, IEEE Transactions on Neural Networks