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Evolving Intelligent Systems (Methodology and Applications) || Online Identification of Self-Organizing Fuzzy Neural Networks for Modeling Time-Varying Complex Systems

✍ Scribed by Angelov, Plamen; Filev, Dimitar P.; Kasabov, Nikola

Book ID
118050778
Publisher
John Wiley & Sons, Inc.
Year
2010
Tongue
English
Weight
884 KB
Edition
1
Category
Article
ISBN
0470287195

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✦ Synopsis

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.