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OverviewWhereas computer systems can easily handle even complicated and nonlinear mathematical models, human information processing is mainly based on linguistic knowledge. So the main advantage of using linguistic terms even with vague ranges is the intuitive interpretability of linguistic rules.Ishibuchi and his coauthors explain how classification and modeling can be handled in a human-understandable manner. They design a framework that can extract linguistic knowledge from numerical data by first identifying linguistic terms, then combining these terms into linguistic rules, and finally constructing a rule set from these linguistic rules. They combine their approach with state-of-the-art soft computing techniques such as multi-objective genetic algorithms, genetics-based machine learning, and fuzzified neural networks. Finally they demonstrate the usability of the combined techniques with various simulation results.In this largely self-contained volume, students specializing in soft computing will appreciate the detailed presentation, carefully discussed algorithms, and the many simulation experiments, while researchers will find a wealth of new design schemes, thorough analysis, and inspiring new research. Full Product DetailsAuthor: Hisao Ishibuchi , Tomoharu Nakashima , Manabu NiiPublisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K Edition: 2005 ed. Dimensions: Width: 15.50cm , Height: 1.90cm , Length: 23.50cm Weight: 1.380kg ISBN: 9783540207672ISBN 10: 3540207678 Pages: 308 Publication Date: 19 November 2004 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsLinguistic Information Granules.- Pattern Classification with Linguistic Rules.- Learning of Linguistic Rules.- Input Selection and Rule Selection.- Genetics-Based Machine Learning.- Multi-Objective Design of Linguistic Models.- Comparison of Linguistic Discretization with Interval Discretization.- Modeling with Linguistic Rules.- Design of Compact Linguistic Models.- Linguistic Rules with Consequent Real Numbers.- Handling of Linguistic Rules in Neural Networks.- Learning of Neural Networks from Linguistic Rules.- Linguistic Rule Extraction from Neural Networks.- Modeling of Fuzzy Input—Output Relations.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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