Evolving Intelligent Systems : Methodology and Applications

by Editor: Plamen Parvanov Angelov (Department of Communication Systems, Lancaster University ); Editor: Dimitar P. Filev (Ford Motor Company, AMTDC, Redford, Michigan); Editor: Nik Kasabov (Knowledge Engineering and Discovery Research Institute and

• ISBN13: 9780470569955
• ISBN10: 0470569956
• Format: eBook
• Copyright: 2010-03-01
• Publisher: Wiley-IEEE Press

Summary

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.

Table of Contents

Preface

Evolving Intelligent Systems

The Editors

Methodology

Evolving Fuzzy Systems

Learning Methods for Evolving Intelligent Systems

Evolving Takagi-Sugeno Fuzzy Systems from Data Streams (eTS+)

Fuzzy Models of Evolvable Granularity

Evolving Fuzzy Modeling Using Participatory Learning

Towards Robust and Transparent Evolving Fuzzy Systems

The building of fuzzy systems in real-time: towards interpretable fuzzy rules

Evolving Neuro-Fuzzy Systems

On-line Feature Selection for Evolving Intelligent Systems

Stability Analysis of an On-Line Evolving Neuro-Fuzzy Network

On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems

Data Fusion via Fission for the Analysis of Brain Death

Evolving Fuzzy Clustering and Classification

Similarity Analysis and Knowledge Acquisition by Use of Evolving Neural Models and Fuzzy Decision

An Extended version of Gustafson-Kessel Clustering Algorithm for Evolving Data Stream Clustering

Evolving Fuzzy Classification of Non-Stationary Time Series

Applications Of Eis

Evolving Intelligent Sensors in Chemical Industry

Recognition of Human Grasps by Fuzzy Modeling

Evolutionary Architecture for Lifelong Learning and Real-time Operation in Autonomous Robots

Applications of Evolving Intelligent Systems to Oil and Gas Industry

Conclusion

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