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Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering

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Here we have a comprehensive, problem-oriented, engineering perspective on the uses of neural nets, fuzzy systems, and hybrids that emphasizes practical solutions to everyday artificial intelligence (AI) problems over abstract theoretical noodling. Intended for upper-division students and postgraduates who need a solid grounding in knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering is still useful as a reference for professionals and even as a text for advanced students in the lower levels.

Taking problems as diverse as soil classification and speech recognition, Kasabov shows the relative merits and failings of the different architectures and hybrids through examples and problems to solve. Organized as a textbook, the first two chapters cover the field as a whole and present traditional AI approaches to knowledge engineering. Subsequent chapters examine the particulars of fuzzy systems, neural networks, hybrids, and new models.

Foundations assumes a good understanding of undergraduate-level mathematics; those who wish to fully explore the problems on their own can obtain the requisite software for free through anonymous FTP. --Rob Lightner

"Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems."
-- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati

Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems.

In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples.

Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material.

Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

About the Author
Nikola K. Kasabov is Associate Professor in Information Science, University of Otago, New Zealand.

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