Evolving Intelligent Systems

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Methodology and Applications
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Book description

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.

Detailed info
Age restriction:
0+
Date added to LitRes:
03 October 2018
Size:
462 pp.
ISBN:
9780470569955
Total size:
12 MB
Total number of pages:
462
Page size:
156 x 235 мм
Editors:
Dimitar P. Filev, Nik Kasabov, Plamen Angelov
Publisher:
Wiley
Copyright:
John Wiley & Sons Limited
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