Evolving Intelligent Systems: Methodology and Applications by Plamen Angelov, Dimitar P. Filev, Nik Kasabov
English | 2010 | ISBN: 0470287195 | 444 pages | PDF | 6,5 MB
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.

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