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PREFACE. <p>Evolving Intelligent Systems.</p> <p>The Editors.</p> <p><b>PART I: METHODOLOGY.</b></p> <p><b>Evolving Fuzzy Systems.</b></p> <p>1. Learning Methods for Evolving Intelligent Systems (<i>R. Yager</i>).</p> <p>2. Evolving Takagi-Sugeno Fuzzy Systems from Data Streams (eTS+) (<i>P. Angelov</i>).</p> <p>3. Fuzzy Models of Evolvable Granularity (<i>W. Pedrycz</i>).</p> <p>4. Evolving Fuzzy Modeling Using Participatory Learning (<i>E. Lima, M. Hell, R. Ballini, and F. Gomide</i>).</p> <p>5. Towards Robust and Transparent Evolving Fuzzy Systems (<i>E. Lughofer</i>).</p> <p>6. The building of fuzzy systems in real-time: towards interpretable fuzzy rules (<i>A. Dourado, C. Pereira, and V. Ramos</i>).</p> <p><b>Evolving Neuro-Fuzzy Systems.</b></p> <p>7. On-line Feature Selection for Evolving Intelligent Systems (<i>S. Ozawa, S. Pang, and N. Kasabov</i>).</p> <p>8. Stability Analysis of an On-Line Evolving Neuro-Fuzzy Network (<i>J. de J. Rubio Avila</i>).</p> <p>9. On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems (<i>G. Prasad, T. M. McGinnity, and G. Leng</i>).</p> <p>10. Data Fusion via Fission for the Analysis of Brain Death (<i>L. Li, Y. Saito, D. Looney, T. Tanaka, J. Cao, and D. Mandic</i>).</p> <p><b>Evolving Fuzzy Clustering and Classification.</b></p> <p>11. Similarity Analysis and Knowledge Acquisition by Use of Evolving Neural Models and Fuzzy Decision (<i>G. Vachkov</i>).</p> <p>12. An Extended version of Gustafson-Kessel Clustering Algorithm for Evolving Data Stream Clustering (<i>D. Filev, and O. Georgieva</i>).</p> <p>13. Evolving Fuzzy Classification of Non-Stationary Time Series (Y. Bodyanskiy, Y. Gorshkov, I. Kokshenev, and V. Kolodyazhniy).</p> <p><b>PART II: APPLICATIONS OF EIS.</b></p> <p>14. Evolving Intelligent Sensors in Chemical Industry (<i>A. Kordon et al.</i>).</p> <p>15. Recognition of Human Grasps by Fuzzy Modeling (R Palm, B Kadmiry, and B Iliev).</p> <p>16. Evolutionary Architecture for Lifelong Learning and Real-time Operation in Autonomous Robots (<i>R. J. Duro, F. Bellas and J.A. Becerra</i>) 17. Applications of Evolving Intelligent Systems to Oil and Gas Industry (<i>J. J. Macias Hernandez et al.</i>).</p> <p>Conclusion.</p>

<p>PLAMEN 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.</p> <p>DIMITAR P. FILEV, PhD, is a Senior Technical Leader, Intelligent Control & Information Systems, with Ford Research & 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).</p> <p>Nikola 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).</p>

<p>From theory to techniques, the first all-in-one resource for EIS</p> <p>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.</p> <ul> <li> <p>Explains the following fundamental approaches for developing evolving intelligent systems (EIS):</p> </li> <li style="list-style: none"> <ul> <li>the Hierarchical Prioritized Structure</li> <li> <p>the Participatory Learning Paradigm</p> </li> <li> <p>the Evolving Takagi-Sugeno fuzzy systems (eTS+)</p> </li> <li> <p>the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm</p> </li> </ul> </li> <li> <p>Emphasizes the importance and increased interest in online processing of data streams</p> </li> <li> <p>Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation</p> </li> <li> <p>Presents a methodology for developing robust and interpretable evolving fuzzy rule-based systems</p> </li> <li> <p>Introduces an integrated approach to incremental (real-time) feature extraction and classification</p> </li> <li> <p>Proposes a study on the stability of evolving neuro-fuzzy recurrent networks</p> </li> <li> <p>Details methodologies for evolving clustering and classification</p> </li> <li> <p>Reveals different applications of EIS to address real problems in areas of:</p> </li> <li style="list-style: none"> <ul> <li> <p>evolving inferential sensors in chemical and petrochemical industry</p> </li> <li> <p>learning and recognition in robotics</p> </li> </ul> </li> <li> <p>Features downloadable software resources</p> </li> </ul> <p>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.</p>

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