Evolving Intelligent Systems: Methodology and Applications
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...
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From theory to techniques, the first all-in-one resource for EISThere 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.
vii1 Learning Methods for Evolving Intelligent Systems Ronald R. Yager 12 Evolving Takagi-Sugeno Fuzzy Systems from Streaming Data (eTS+) Plamen Agelov 213 Fuzzy Models of Evolvable Granularity Witold Pedrycz 514 Evolving Fuzzy Modeling Using Participatory Learning E. Lima M. Hell R. Ballini F. Gomide 675 Toward Robust Evolving Fuzzy Systems Edwin Lughofer 876 Building Interpretable Systems in Real Time José V. Ramos Carlos Pereira António Dourado 1277 Online Feature Extraction for Evolving Intelligent Systems S. Ozawas S. Pang N. Kasabov 1518 Stability Analysis for an Online Evolving Neuro-Fuzzy Recurrent Network José de Jesú Rubio 1739 Online Identification of Self-Organizing Fizzy Neural Networks for Modeling Time-Varying Complex Systems G. Prasad G. Leng T. M. McGinnity D. Coyle 20110 Data Fusion Via Fission for the Analysis of Brain Death L. Li Y. Saito D. Looney T. Tanaka J. Cao D. Mandic 22911 Similarity Analysis and Knowledge Acquisition by use of Evolving Neural Models and Fuzzy Decision Gancho Vachkov 24712 An Extended Version of The Gustafson-Kessel Algorithm for Evolving Data Stream Clustering Dimitar Filev Olga Georgieva 27313 Evolving Fuzzy Classification of Nonstationary Time Series Ye. Bodyanskiy Ye. Gorshkov I. Kokshenev V. Kolodyazhniy 30114 Evolving Inferential Sensors in The Chemical Process Industry Plamen Angelov Arthur Kordon 31315 Recognition of Human Grasp By Fuzzy Modeling R. Palm B. Kadmiry B. Iliev 33716 Evolutionary Architecture for Lifelong Learning and Real-Time Operation in Autonomous Robots R. J. Duro F. Bellas J. A. Becerra 36517 Applications of Evolving Intelligent Systems To Oil and Gas Industry José Macías-Hernández Plamen Angelov 401Epilogue 423About the Editors 425About the Contributors 427Index 439
