Evolving Intelligent Systems

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.

From theory to techniques, the first all-in-one resource forEISThere is a clear demand in advanced process industries, defenseand Internet and communication (VoIP) applications for intelligentyet adaptive/evolving systems. Evolving Intelligent Systems is thefirst self- contained volume that covers this newly establishedconcept in its entirety, from a systematic methodology to casestudies to industrial applications. Featuring chapters written byleading world experts, it addresses the progress, trends, and majorachievements in this emerging research field, with a strongemphasis on the balance between novel theoretical results andsolutions and practical real-life applications.* Explains the following fundamental approaches for developingevolving 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-knownGustafson-Kessel offline clustering algorithm* Emphasizes the importance and increased interest in onlineprocessing of data streams* Outlines the general strategy of using the fuzzy dynamicclustering as a foundation for evolvable informationgranulation* Presents a methodology for developing robust and interpretableevolving 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-fuzzyrecurrent networks* Details methodologies for evolving clustering andclassification* Reveals different applications of EIS to address real problemsin areas of:* * evolving inferential sensors in chemical and petrochemicalindustry* learning and recognition in robotics* Features downloadable software resourcesEvolving Intelligent Systems is the one-stop reference guide forboth theoretical and practical issues for computer scientistsengineers, researchers, applied mathematicians, machine learningand data mining experts, graduate students, and professionals.

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