Free Download | Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering

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The symbolic AI systems have been associated in the last decades with two main issues—the representation issue and the processing (reasoning) issue. They have proved effective in handling problems characterized by exact and complete representation. Their reasoning methods are sequential by nature. Typical AI techniques are propositional logic, predicate logic, and production systems. However, the symbolic AI systems have very little power in dealing with inexact, uncertain, corrupted, imprecise, or ambiguous information. Neural networks and fuzzy systems are different approaches to introducing human like reasoning to knowledge-based intelligent systems. They represent different paradigms of information processing, but they have similarities that make their common teaching, reading, and practical use quite natural and logical. Both paradigms have been useful for representing inexact, incomplete, corrupted data, and for approximate reasoning over uncertain knowledge. Fuzzy systems, which are based on Zadeh's fuzzy logic theory, are effective in representing explicit but ambiguous commonsense knowledge, whereas neural networks provide excellent facilities for approximating data, learning knowledge from data, approximate reasoning, and parallel processing. Evidence from research on the brain shows that the way we think is formed by sequential and parallel processes. Knowledge engineering benefits greatly from combining symbolic, neural computation, and fuzzy computation.