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Evolving Intelligent Systems

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9780070428072
9780262012119
9780387310732
Alpaydin
Category=UYQ
computational intelligence
data density
data stream
data streaming
drift
E. Alpaydin
EIS
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eTS+
Evolving Clustering Algorithm
evolving intelligence
evolving intelligent systems
feature selection
fuzzy rule-based
fuzzy system
granularity
Gustafson-Kessel
Hierarchical Prioritized Structure
hierarchical structure
ieee
ieee book
ieee series
intelligent sensors
interpretability
introduction to machine learning
machine intelligence
machine learning
neural network
neuro-fuzzy
on-line processing
outliers
participatory learning
Participatory Learning Paradigm
pattern recognition
recursive covariance calculation
robotics
self-developing
self-learning
sensing network
stability
stationary signals
T. M. Mitchell
Takagi-Sugeno

Product details

  • ISBN 9780470287194
  • Weight: 780g
  • Dimensions: 163 x 244mm
  • Publication Date: 16 Apr 2010
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.
About

PLAMEN ANGELOV, PhD, is with the Department of Communication Systems, Lancaster University. He is a member of the Fuzzy Systems Technical Committee, the founding Chair of the Adaptive Fuzzy Systems Task Force to the Computational Intelligence Society, and a Senior Member of IEEE.

DIMITAR P. FILEV, PhD, is a Senior Technical Leader, Intelligent Control & Information Systems, with Ford Research & Advanced Engineering and a Fellow of IEEE. He is a Vice President for Cybernetics of the IEEE Systems, Man, and Cybernetics Society and?past president of the North American Fuzzy Information Processing Society (NAFIPS).

Nikola Kasabov is the Director of the Knowledge Engineering and Discovery Research Institute (KEDRI). He holds a Chair of Knowledge Engineering at the School of Computer and Information Sciences at Auckland University of Technology. He is a Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the New Zealand Computer Society, and the President of the International Neural Network Society (INNS).