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 (Vo IP) 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 (e TS+)
* 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.
Daftar Isi
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 (e TS+)
(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: towards
interpretable 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 Neural
Networks for Modelling Time-varying Complex Systems (G. Prasad,
T. M. Mc Ginnity, 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 of
Evolving Neural Models and Fuzzy Decision (G. Vachkov).
12. An Extended version of Gustafson-Kessel Clustering Algorithm
for 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, B
Kadmiry, and B Iliev).
16. Evolutionary Architecture for Lifelong Learning and
Real-time Operation in Autonomous Robots (R. J. Duro, F. Bellas
and J.A. Becerra) 17. Applications of Evolving Intelligent
Systems to Oil and Gas Industry (J. J. Macias Hernandez et
al.).
Conclusion.
Tentang Penulis
PLAMEN ANGELOV, Ph D, 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, Ph D, 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).
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