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Development of a Self-Learning Approach: Applied to Pattern Recognition and Fuzzy Control - Softcover
Synopsis
Systems based on fuzzy rules are widely used in the development of control, pattern recognition and machine intelligence systems. The structure of the fuzzy rule base is the most influential factor for the performance of fuzzy rule-based systems. This structure is defined by the number of fuzzy partitions and the formation relations used. The number and structure of the fuzzy partitions have to be defined during the design process. Thus, a large number of rules have to be generated. To avoid large computational costs, a reduction process is required. Current procedures for the design of fuzzy-based systems often do not take into account the signal or data specifications for the system considered. In the present study, a new approach for building a fuzzy rule-based system is developed. In this approach, design of the fuzzy rule base is based on the statistical properties of the data considered. A new framework is developed for automated and improved generation of fuzzy-based rules for identification and classification processes. Different benchmark data, comparative approaches, practical application , and hypothesis techniques are used to evaluate the effectiveness of this approach.
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About the Author
He was awarded a Dr.-Ing. degree in engineering sciences at the University of Duisburg-Essen, Germany in 2012. His research interests include fuzzy systems, pattern recognition, machine learning, data mining, signal and image processing, intelligent systems, statistical analysis, adaptation and diagnosis.
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Development of a Self-Learning Approach
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Systems based on fuzzy rules are widely used in the development of control, pattern recognition and machine intelligence systems. The structure of the fuzzy rule base is the most influential factor for the performance of fuzzy rule-based systems. This structure is defined by the number of fuzzy partitions and the formation relations used. The number and structure of the fuzzy partitions have to be defined during the design process. Thus, a large number of rules have to be generated. To avoid large computational costs, a reduction process is required. Current procedures for the design of fuzzy-based systems often do not take into account the signal or data specifications for the system considered. In the present study, a new approach for building a fuzzy rule-based system is developed. In this approach, design of the fuzzy rule base is based on the statistical properties of the data considered. A new framework is developed for automated and improved generation of fuzzy-based rules for identification and classification processes. Different benchmark data, comparative approaches, practical application , and hypothesis techniques are used to evaluate the effectiveness of this approach. 100 pp. Englisch. Seller Inventory # 9783838137155
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Development of a Self-Learning Approach
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Aljoumaa HammoudHe was awarded a Dr.-Ing. degree in engineering sciences at the University of Duisburg-Essen, Germany in 2012. His research interests include fuzzy systems, pattern recognition, machine learning, data mining, signal a. Seller Inventory # 5407959
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Development of a Self-Learning Approach
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ISBN 10: 3838137159
ISBN 13: 9783838137155
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Systems based on fuzzy rules are widely used in the development of control, pattern recognition and machine intelligence systems. The structure of the fuzzy rule base is the most influential factor for the performance of fuzzy rule-based systems. This structure is defined by the number of fuzzy partitions and the formation relations used. The number and structure of the fuzzy partitions have to be defined during the design process. Thus, a large number of rules have to be generated. To avoid large computational costs, a reduction process is required. Current procedures for the design of fuzzy-based systems often do not take into account the signal or data specifications for the system considered. In the present study, a new approach for building a fuzzy rule-based system is developed. In this approach, design of the fuzzy rule base is based on the statistical properties of the data considered. A new framework is developed for automated and improved generation of fuzzy-based rules for identification and classification processes. Different benchmark data, comparative approaches, practical application , and hypothesis techniques are used to evaluate the effectiveness of this approach.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 100 pp. Englisch. Seller Inventory # 9783838137155
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Development of a Self-Learning Approach : Applied to Pattern Recognition and Fuzzy Control
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ISBN 10: 3838137159
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Systems based on fuzzy rules are widely used in the development of control, pattern recognition and machine intelligence systems. The structure of the fuzzy rule base is the most influential factor for the performance of fuzzy rule-based systems. This structure is defined by the number of fuzzy partitions and the formation relations used. The number and structure of the fuzzy partitions have to be defined during the design process. Thus, a large number of rules have to be generated. To avoid large computational costs, a reduction process is required. Current procedures for the design of fuzzy-based systems often do not take into account the signal or data specifications for the system considered. In the present study, a new approach for building a fuzzy rule-based system is developed. In this approach, design of the fuzzy rule base is based on the statistical properties of the data considered. A new framework is developed for automated and improved generation of fuzzy-based rules for identification and classification processes. Different benchmark data, comparative approaches, practical application , and hypothesis techniques are used to evaluate the effectiveness of this approach. Seller Inventory # 9783838137155
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Development of a Self-Learning Approach | Applied to Pattern Recognition and Fuzzy Control
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Taschenbuch. Condition: Neu. Development of a Self-Learning Approach | Applied to Pattern Recognition and Fuzzy Control | Hammoud Aljoumaa | Taschenbuch | 100 S. | Englisch | 2015 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838137155 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 105583927
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