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Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms (Studies in Fuzziness and Soft Computing, 86) - Hardcover
Synopsis
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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From the Back Cover
This book presents new concepts and implementations of Computational Intelligence (CI) systems (based on neuro-fuzzy and fuzzy neural synergisms) and a broad comparative analysis with the best-known existing neuro-fuzzy systems as well as with systems representing other knowledge-discovery techniques such as rough sets, decision trees, regression trees, probabilistic rule induction etc. This presentation is preceded by a discussion of the main directions of synthesizing fuzzy sets, artificial neural networks and genetic algorithms in the framework of designing CI systems. In order to keep the book self-contained, introductions to the basic concepts of fuzzy systems, artificial neural networks and genetic algorithms are given. This book is intended for researchers and practitioners in AI/CI fields and for students of computer science or neighbouring areas.
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- PublisherPhysica
- Publication date2001
- ISBN 10 3790814393
- ISBN 13 9783790814392
- BindingHardcover
- LanguageEnglish
- Number of pages374
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Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms (Studies in Fuzziness and Soft Computing, 86)
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Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms (Studies in Fuzziness and Soft Computing, 86)
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Computational Intelligence Systems and Applications : Neuro-Fuzzy and Fuzzy Neural Synergisms
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Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms (Studies in Fuzziness and Soft Computing, 86)
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Computational Intelligence Systems and Applications
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Springer, Berlin, Physica-Verlag HD, Physica Dez 2001, 2001
ISBN 10: 3790814393
ISBN 13: 9783790814392
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 364 pp. Englisch. Seller Inventory # 9783790814392
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Computational Intelligence Systems and Applications: Neuro-Fuzzy and Fuzzy Neural Synergisms (Studies in Fuzziness and Soft Computing, 86)
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Self-contained presentation of new concepts and structures of CI systems and their real-life applicationsTraditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in ha. Seller Inventory # 5310389
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783790814392
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