Neural and Adaptive Systems

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Develop New Insight into the Behavior of Adaptive Systems This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and adaptive filters into a common framework. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction. The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions. This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples. Each of these examples is 'live, ' allowing the user to change parameters and experiment first-hand with real-world adaptive systems. This creates a powerful environment for learning through both visualization and experimentation. Key Features of the Text * The text and CD combine to become an interactive learning tool. * Emphasis is on understanding the behavior of adaptive systems rather than mathematical derivations. * Each key concept is followed by an interactive example. * Over 200 fully functional simulations of adaptive systems are included. * The text and CD offer a unified view of neural networks, adaptive filters, pattern recognition, and support vector machines. * Hyperlinks allow instant access to keyword definitions, bibliographic references, equations, and advanced discussions of concepts. The CD-ROM Contains: * A complete, electronic version of the text in hypertext format * NeuroSolutions, an industry standard, icon-based neural network/adaptive system simulator * A tutorial on how to use NeuroSolutions * Additional data files to use with the simulator "An innovative approach to describing neurocomputing and adaptive learning systems from a perspective which unifies classical linear adaptive systems approaches with the modern advances in neural networks. It is rich in examples and practical insight." -James Zeidler, University of California, San Diego