Evolving Intelligent Systems Methodology And Applications Ieee Press Series On Computational Intelligence 1st Edition Plamen Angelov by Plamen Angelov, Dimitar P. Filev, Nik Kasabov 9780470287194, 0470287195 instant download after payment.

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 Structurethe Participatory Learning Paradigmthe Evolving Takagi-Sugeno fuzzy systems (eTS+)the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithmEmphasizes 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 granulationPresents a methodology for developing robust and interpretable evolving fuzzy rule-based systemsIntroduces an integrated approach to incremental (real-time) feature extraction and classificationProposes a study on the stability of evolving neuro-fuzzy recurrent networksDetails methodologies for evolving clustering and classificationReveals different applications of EIS to address real problems in areas of:evolving inferential sensors in chemical and petrochemical industrylearning and recognition in roboticsFeatures 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.