Angelov / Filev / Kasabov Evolving Intelligent Systems

Methodology and Applications
1. Auflage 2010
ISBN: 978-0-470-56995-5
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 464 Seiten, E-Book

Reihe: IEEE Press Series on Computational Intelligence

From theory to techniques, the first all-in-one resource forEIS
There is a clear demand in advanced process industries, defense,and Internet and communication (VoIP) applications for intelligentyet adaptive/evolving systems. Evolving Intelligent Systems is thefirst self- contained volume that covers this newly establishedconcept in its entirety, from a systematic methodology to casestudies to industrial applications. Featuring chapters written byleading world experts, it addresses the progress, trends, and majorachievements in this emerging research field, with a strongemphasis on the balance between novel theoretical results andsolutions and practical real-life applications.
* Explains the following fundamental approaches for developingevolving 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-knownGustafson-Kessel offline clustering algorithm
* Emphasizes the importance and increased interest in onlineprocessing of data streams
* Outlines the general strategy of using the fuzzy dynamicclustering as a foundation for evolvable informationgranulation
* Presents a methodology for developing robust and interpretableevolving 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-fuzzyrecurrent networks
* Details methodologies for evolving clustering andclassification
* Reveals different applications of EIS to address real problemsin areas of:
* * evolving inferential sensors in chemical and petrochemicalindustry
* learning and recognition in robotics
* Features downloadable software resources
Evolving Intelligent Systems is the one-stop reference guide forboth theoretical and practical issues for computer scientists,engineers, researchers, applied mathematicians, machine learningand data mining experts, graduate students, and professionals.

Autoren/Hrsg.

Angelov, Plamen

Filev, Dimitar P.

Kasabov, Nik

Inhaltsverzeichnis

PREFACE.
Evolving Intelligent Systems.
The Editors.
PART I: METHODOLOGY.
Evolving Fuzzy Systems.
1. Learning Methods for Evolving Intelligent Systems (R.Yager).
2. Evolving Takagi-Sugeno Fuzzy Systems from Data Streams (eTS+)(P. Angelov).
3. Fuzzy Models of Evolvable Granularity (W.Pedrycz).
4. Evolving Fuzzy Modeling Using Participatory Learning (E.Lima, M. Hell, R. Ballini, and F. Gomide).
5. Towards Robust and Transparent Evolving Fuzzy Systems (E.Lughofer).
6. The building of fuzzy systems in real-time: towardsinterpretable fuzzy rules (A. Dourado, C. Pereira, and V.Ramos).
Evolving Neuro-Fuzzy Systems.
7. On-line Feature Selection for Evolving Intelligent Systems(S. Ozawa, S. Pang, and N. Kasabov).
8. Stability Analysis of an On-Line Evolving Neuro-Fuzzy Network(J. de J. Rubio Avila).
9. On-line Identification of Self-organizing Fuzzy NeuralNetworks for Modelling Time-varying Complex Systems (G. Prasad,T. M. McGinnity, and G. Leng).
10. Data Fusion via Fission for the Analysis of Brain Death(L. Li, Y. Saito, D. Looney, T. Tanaka, J. Cao, and D.Mandic).
Evolving Fuzzy Clustering and Classification.
11. Similarity Analysis and Knowledge Acquisition by Use ofEvolving Neural Models and Fuzzy Decision (G. Vachkov).
12. An Extended version of Gustafson-Kessel Clustering Algorithmfor Evolving Data Stream Clustering (D. Filev, and O.Georgieva).
13. Evolving Fuzzy Classification of Non-Stationary Time Series(Y. Bodyanskiy, Y. Gorshkov, I. Kokshenev, and V.Kolodyazhniy).
PART II: APPLICATIONS OF EIS.
14. Evolving Intelligent Sensors in Chemical Industry (A.Kordon et al.).
15. Recognition of Human Grasps by Fuzzy Modeling (R Palm, BKadmiry, and B Iliev).
16. Evolutionary Architecture for Lifelong Learning andReal-time Operation in Autonomous Robots (R. J. Duro, F. Bellasand J.A. Becerra) 17. Applications of Evolving IntelligentSystems to Oil and Gas Industry (J. J. Macias Hernandez etal.).
Conclusion.

Über Autor(innen)

PLAMEN ANGELOV, PhD, is with the Department of CommunicationSystems, Lancaster University. He is a member of the Fuzzy SystemsTechnical Committee, the founding Chair of the Adaptive FuzzySystems Task Force to the Computational Intelligence Society, and aSenior Member of IEEE.
DIMITAR P. FILEV, PhD, is a Senior Technical Leader, IntelligentControl & Information Systems, with Ford Research &Advanced Engineering and a Fellow of IEEE. He is a Vice Presidentfor Cybernetics of the IEEE Systems, Man, and Cybernetics Societyand?past president of the North American Fuzzy InformationProcessing Society (NAFIPS).
Nikola Kasabov is the Director of the Knowledge Engineering andDiscovery Research Institute (KEDRI). He holds a Chair of KnowledgeEngineering at the School of Computer and Information Sciences atAuckland University of Technology. He is a Fellow of IEEE, Fellowof the Royal Society of New Zealand, Fellow of the New ZealandComputer Society, and the President of the International NeuralNetwork Society (INNS).