by Nikola K. Kasabov
published by The MIT Press
pages 581
The human brain, consisting of 1011 neurons, realizes intelligent information processing based on exact and commonsense reasoning. Scientists have been trying to implement human intelligence in computers in various ways. Artificial intelligence (AI) pursues exact logical reasoning based on symbol manipulation. Fuzzy engineering uses analog values to realize fuzzy but robust and efficient reasoning. They are macroscopic ways to realize human intelligence at the level of symbols and rules. Neural networks are a microscopic approach to the intelligence of the brain in which information is represented by excitation patterns of neurons.
All of these approaches are partially successful in implementing human intelligence, but are still far from the real one. AI uses mathematically rigorous logical reasoning but is not flexible and is difficult to implement. Fuzzy systems provide convenient and flexible methods of reasoning at the sacrifice of depth and exactness. Neural networks use learning and self-organizing ability but are difficult for handling symbolic reasoning. The point is how to design computerized reasoning, taking account of these methods.
This book solves this problem by combining the three techniques to minimize their weaknesses and enhance their strong points. The book begins with an excellent introduction to AI, fuzzy-, and neuroengineering. The author succeeds in explaining the fundamental ideas and practical methods of these techniques by using many familiar examples. The reason for his success is that the book takes a problem-driven approach by presenting problems to be solved and then showing ideas of how to solve them, rather than by following the traditional theorem-proof style. The book provides an understandable approach to knowledge-based systems for problem solving by combining different methods of AI, fuzzy systems, and neural networks.
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