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 a live,a 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