Evolving Intelligent Systems: Methodology and ApplicationsISBN: 978-0-470-28719-4
Hardcover
464 pages
March 2010, Wiley-IEEE Press
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From theory to techniques, the first all-in-one resource for EIS
There is a clear demand in advanced process industries, defense, and Internet and communication (VoIP) applications for intelligent yet adaptive/evolving systems. Evolving Intelligent Systems is the first self- contained volume that covers this newly established concept in its entirety, from a systematic methodology to case studies to industrial applications. Featuring chapters written by leading world experts, it addresses the progress, trends, and major achievements in this emerging research field, with a strong emphasis on the balance between novel theoretical results and solutions and practical real-life applications.
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Explains the following fundamental approaches for developing evolving intelligent systems (EIS):
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- the Hierarchical Prioritized Structure
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the Participatory Learning Paradigm
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the Evolving Takagi-Sugeno fuzzy systems (eTS+)
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the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm
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Emphasizes the importance and increased interest in online processing of data streams
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Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation
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Presents a methodology for developing robust and interpretable evolving fuzzy rule-based systems
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Introduces an integrated approach to incremental (real-time) feature extraction and classification
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Proposes a study on the stability of evolving neuro-fuzzy recurrent networks
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Details methodologies for evolving clustering and classification
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Reveals different applications of EIS to address real problems in areas of:
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evolving inferential sensors in chemical and petrochemical industry
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learning and recognition in robotics
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Features downloadable software resources
Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.