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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms

Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms

Gorzalczany, Marian B.

Hardcover

Series: Studies in Fuzziness and Soft Computing, Book 86

Medical ReferenceGeneral ComputersGeneral Science

ISBN10: 3790814393
ISBN13: 9783790814392
Publisher: Springer Nature
Published: Dec 14 2001
Pages: 364
Weight: 1.56
Height: 0.88 Width: 6.14 Depth: 9.21
Language: English
Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real- world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge buried in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge encoded in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.

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