A new book on neuro-fuzzy computation

[Nik Kasabov]

"Neuro-Fuzzy Techniques for Intelligent Information Systems"
Nikola Kasabov and Robert Kozma (eds)

January 1999, Physica -Verlag (Springer Verlag), Berlin, Germany 
ISBN 3-7908-1187-4, DM 178-, fax: +49 (30) 8 2787301, 
email: orders at springer.de, http://www.springer.de/
P.O.Box 140201, D-14302 Berlin, Germany


Abstract
This edited volume comprises selected chapters that cover contemporary
issues of the development and the application of neuro-fuzzy techniques.
Developing and using neural networks, fuzzy logic systems, genetic
algorithms and statistical methods as separate techniques, or in their
combination, have been research topics in several areas such as Mathematics,
Engineering, Computer Science, Physics, Economics and Finance.Here the
latest results in this field are presented from both theoretical and
practical points of view. The volume has four main parts. Part one presents
generic techniques and theoretical issues, while part two, three and four
deal with practically oriented models, systems and implementations.

 
Content: 
Part 1: Generic Neuro-Fuzzy and Hybrid Techniques
Chapter 1. Analysis and Modelling of Complex Systems Using the
Self-Organising Map (O.Simula, J.Vesanto, E.Alhoniemi, J.Hollmen) 
Chapter 2. Fuzzy Methods for Learning from Data (V.Cherkassky)
Chapter 3. Uneven Allocation of Membership Functions for Fuzzy Modelling of
Multi-Input Systems (K.Tachibana,T.Furuhashi)
Chapter 4. Fuzzy Equivalence Relations and Fuzzy Partitions(B.Reusch)
Chapter 5. Identifying Fuzzy Rule-Based Models utilising Neural Networks,
Fuzzy Logic and Genetic Algorithms (A.Bastian)
Chapter 6. Neuro-Genetic Information Processing for Optimisation and
Adaptation in Intelligent Systems (M.Watts,N.Kasabov)
Chapter 7. Evolving Connectionist and Fuzzy Connectionist Systems: Theory
and Applications for Adaptive, On-line Intelligent Systems (N.Kasabov)

Part 2. Neuro-Fuzzy Systems for Pattern Recognition, Image-,  Speech- and
Language Processing
Chapter 8. Connectionist Approaches for Feature Analysis (N.Pal)
Chapter 9. Pattern Classification and Feature Selection by Ellipsoidal Fuzzy
Rules  (S.Abe)
Chapter 10. Printed Chinese Optical Character Recognition by Neural Networks
Y.Wu,M.Zhao)
Chapter 11. Image Processing by Chaotic Neural Network Fuzzy Membership
Functions (H.Szu,C.Hsu) 
Chapter 12. Fuzzy Learning Machine with Application to the Detection of
Landmarks for Orthodontic Treatment (E.Uchino,T.Yamakawa)
Chapter 13. Speech Data Analysis and Recognition Using Fuzzy Neural Networks
and Self-Organising Maps (N.Kasabov, R.Kozma, R.Kilgour et al)
Chapter 14. Connectionist Methods for Stylometric Analysis: A Hybrid
Approach
(D.Kassabova,P.Sallis)

Part 3. Neuro-Fuzzy Systems for Information Retrieval and Socio-Economic
Applications
Chapter 15. Soft Information Retrieval: Applications of Fuzzy Set Theory and
Neural Networks (F.Crestani,G.Pasi)
Chapter 16. Modeling Consensus in Group Decision Making: a Fuzzy Dynamical
Approach (Mario Fedrizzi, Michele Fedrizzi, R.A. M Pereira)
Chapter 17. Building fuzzy expert systems (M.Negnevitsky)
Chapter 18. A Neural Network for Fuzzy Dynamic Programming and Its use in
Socio-Economic Regional Development Planning
(J.Kacprzyk,R.Francelin,F.Gomide
Chapter 19. Investment Maps for Emerging Markets (G.Deboeck)
Chapter 20. Adaptive Fuzzy-Impedance Controller for Constrained Robot Motion
(P.Petrovich,V.Milacic)

Part 4. Specialised Hardware for Neuro-Fuzzy Intelligent Systems
Chapter 21. Specialised Hardware for Computational Intelligence (G.Coghill)
Chapter 22. Evolvable Hardware - The Coming Hardware Design Method?
(J.Torrensen)
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Have an enjoyable and prosperous New Year!

Nik Kasabov
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Professor Dr Nikola Kasabov
Department of Information Science
University of Otago,P.O.Box 56 Dunedin,
New Zealand
phone:+64 3 479 8319, fax:  +64 3 479 8311
email: nkasabov at otago.ac.nz
http://kel.otago.ac.nz/nik/
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