{"product_id":"evolving-intelligent-systems-isbn-9780470287194","title":"Evolving Intelligent Systems","description":"\u003cp\u003eFrom theory to techniques, the first all-in-one resource for EIS\u003c\/p\u003e \u003cp\u003eThere 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eExplains the following fundamental approaches for developing evolving intelligent systems (EIS):\u003c\/p\u003e \u003c\/li\u003e \u003cli style=\"list-style: none\"\u003e \u003cul\u003e \u003cli\u003ethe Hierarchical Prioritized Structure\u003c\/li\u003e \u003cli\u003e \u003cp\u003ethe Participatory Learning Paradigm\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ethe Evolving Takagi-Sugeno fuzzy systems (eTS+)\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ethe evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eEmphasizes the importance and increased interest in online processing of data streams\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eOutlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ePresents a methodology for developing robust and interpretable evolving fuzzy rule-based systems\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eIntroduces an integrated approach to incremental (real-time) feature extraction and classification\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eProposes a study on the stability of evolving neuro-fuzzy recurrent networks\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eDetails methodologies for evolving clustering and classification\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eReveals different applications of EIS to address real problems in areas of:\u003c\/p\u003e \u003c\/li\u003e \u003cli style=\"list-style: none\"\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eevolving inferential sensors in chemical and petrochemical industry\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003elearning and recognition in robotics\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eFeatures downloadable software resources\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEvolving 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.\u003c\/p\u003e  PREFACE.  \u003cp\u003eEvolving Intelligent Systems.\u003c\/p\u003e \u003cp\u003eThe Editors.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I: METHODOLOGY.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEvolving Fuzzy Systems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Learning Methods for Evolving Intelligent Systems (\u003ci\u003eR. Yager\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e2. Evolving Takagi-Sugeno Fuzzy Systems from Data Streams (eTS+) (\u003ci\u003eP. Angelov\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e3. Fuzzy Models of Evolvable Granularity (\u003ci\u003eW. Pedrycz\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e4. Evolving Fuzzy Modeling Using Participatory Learning (\u003ci\u003eE. Lima, M. Hell, R. Ballini, and F. Gomide\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e5. Towards Robust and Transparent Evolving Fuzzy Systems (\u003ci\u003eE. Lughofer\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e6. The building of fuzzy systems in real-time: towards interpretable fuzzy rules (\u003ci\u003eA. Dourado, C. Pereira, and V. Ramos\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEvolving Neuro-Fuzzy Systems.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7. On-line Feature Selection for Evolving Intelligent Systems (\u003ci\u003eS. Ozawa, S. Pang, and N. Kasabov\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e8. Stability Analysis of an On-Line Evolving Neuro-Fuzzy Network (\u003ci\u003eJ. de J. Rubio Avila\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e9. On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems (\u003ci\u003eG. Prasad, T. M. McGinnity, and G. Leng\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e10. Data Fusion via Fission for the Analysis of Brain Death (\u003ci\u003eL. Li, Y. Saito, D. Looney, T. Tanaka, J. Cao, and D. Mandic\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEvolving Fuzzy Clustering and Classification.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11. Similarity Analysis and Knowledge Acquisition by Use of Evolving Neural Models and Fuzzy Decision (\u003ci\u003eG. Vachkov\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e12. An Extended version of Gustafson-Kessel Clustering Algorithm for Evolving Data Stream Clustering (\u003ci\u003eD. Filev, and O. Georgieva\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e13. Evolving Fuzzy Classification of Non-Stationary Time Series (Y. Bodyanskiy, Y. Gorshkov, I. Kokshenev, and V. Kolodyazhniy).\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II: APPLICATIONS OF EIS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14. Evolving Intelligent Sensors in Chemical Industry (\u003ci\u003eA. Kordon et al.\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e15. Recognition of Human Grasps by Fuzzy Modeling (R Palm, B Kadmiry, and B Iliev).\u003c\/p\u003e \u003cp\u003e16. Evolutionary Architecture for Lifelong Learning and Real-time Operation in Autonomous Robots (\u003ci\u003eR. J. Duro, F. Bellas and J.A. Becerra\u003c\/i\u003e) 17. Applications of Evolving Intelligent Systems to Oil and Gas Industry (\u003ci\u003eJ. J. Macias Hernandez et al.\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003eConclusion.\u003c\/p\u003e  \u003cp\u003ePLAMEN ANGELOV, PhD, is with the Department of Communication Systems, Lancaster University. He is a member of the Fuzzy Systems Technical Committee, the founding Chair of the Adaptive Fuzzy Systems Task Force to the Computational Intelligence Society, and a Senior Member of IEEE.\u003c\/p\u003e \u003cp\u003eDIMITAR P. FILEV, PhD, is a Senior Technical Leader, Intelligent Control \u0026amp; Information Systems, with Ford Research \u0026amp; Advanced Engineering and a Fellow of IEEE. He is a Vice President for Cybernetics of the IEEE Systems, Man, and Cybernetics Society and?past president of the North American Fuzzy Information Processing Society (NAFIPS).\u003c\/p\u003e \u003cp\u003eNikola Kasabov is the Director of the Knowledge Engineering and Discovery Research Institute (KEDRI). He holds a Chair of Knowledge Engineering at the School of Computer and Information Sciences at Auckland University of Technology. He is a Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the New Zealand Computer Society, and the President of the International Neural Network Society (INNS).\u003c\/p\u003e  \u003cp\u003eFrom theory to techniques, the first all-in-one resource for EIS\u003c\/p\u003e \u003cp\u003eThere 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eExplains the following fundamental approaches for developing evolving intelligent systems (EIS):\u003c\/p\u003e \u003c\/li\u003e \u003cli style=\"list-style: none\"\u003e \u003cul\u003e \u003cli\u003ethe Hierarchical Prioritized Structure\u003c\/li\u003e \u003cli\u003e \u003cp\u003ethe Participatory Learning Paradigm\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ethe Evolving Takagi-Sugeno fuzzy systems (eTS+)\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ethe evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eEmphasizes the importance and increased interest in online processing of data streams\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eOutlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ePresents a methodology for developing robust and interpretable evolving fuzzy rule-based systems\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eIntroduces an integrated approach to incremental (real-time) feature extraction and classification\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eProposes a study on the stability of evolving neuro-fuzzy recurrent networks\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eDetails methodologies for evolving clustering and classification\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eReveals different applications of EIS to address real problems in areas of:\u003c\/p\u003e \u003c\/li\u003e \u003cli style=\"list-style: none\"\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eevolving inferential sensors in chemical and petrochemical industry\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003elearning and recognition in robotics\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eFeatures downloadable software resources\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eEvolving 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.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989180891365,"sku":"NP9780470287194","price":166.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470287194.jpg?v=1761783111","url":"https:\/\/k12savings.com\/products\/evolving-intelligent-systems-isbn-9780470287194","provider":"K12savings","version":"1.0","type":"link"}