Evolving Intelligent Systems

Methodology and Applications

By: Plamen Angelov, Dimitar P. Filev, Nik Kasabov

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eText | 25 March 2010 | Edition Number 1

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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.