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Evolving Intelligent Systems

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Evolving Intelligent Systems

Methodology and Applications
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Description

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. Explains the following fundamental approaches for developing evolving intelligent systems (EIS): the Hierarchical Prioritized Structure the Participatory Learning Paradigm the Evolving Takagi-Sugeno fuzzy systems (eTS+) the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm Emphasizes the importance and increased interest in online processing of data streams Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation Presents a methodology for developing robust and interpretable evolving fuzzy rule-based systems Introduces an integrated approach to incremental (real-time) feature extraction and classification Proposes a study on the stability of evolving neuro-fuzzy recurrent networks Details methodologies for evolving clustering and classification Reveals different applications of EIS to address real problems in areas of: evolving inferential sensors in chemical and petrochemical industry learning and recognition in robotics 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.

Author Biography:

PLAMEN ANGELOV, PhD, is with the Department of CommunicationSystems, Lancaster University. He is a member of the Fuzzy SystemsTechnical Committee, the founding Chair of the Adaptive FuzzySystems Task Force to the Computational Intelligence Society, and aSenior Member of IEEE. DIMITAR P. FILEV, PhD, is a Senior Technical Leader, IntelligentControl & Information Systems, with Ford Research &Advanced Engineering and a Fellow of IEEE. He is a Vice Presidentfor Cybernetics of the IEEE Systems, Man, and Cybernetics Societyand?past president of the North American Fuzzy InformationProcessing Society (NAFIPS). Nikola Kasabov is the Director of the Knowledge Engineering andDiscovery Research Institute (KEDRI). He holds a Chair of KnowledgeEngineering at the School of Computer and Information Sciences atAuckland University of Technology. He is a Fellow of IEEE, Fellowof the Royal Society of New Zealand, Fellow of the New ZealandComputer Society, and the President of the International NeuralNetwork Society (INNS).
Release date Australia
April 16th, 2010
Audience
  • Professional & Vocational
Contributors
  • Edited by Dimitar P. Filev
  • Edited by Nik Kasabov
  • Edited by Plamen Angelov
Illustrations
Charts: 90 B&W, 0 Color; Graphs: 90 B&W, 0 Color
Pages
464
Dimensions
163x244x31
ISBN-13
9780470287194
Product ID
3978026

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