Advances in Neural Networks – ISNN 2004: International by Jianjun Wang, ZongBen Xu, Weijun Xu (auth.), Fu-Liang Yin, PDF

By Jianjun Wang, ZongBen Xu, Weijun Xu (auth.), Fu-Liang Yin, Jun Wang, Chengan Guo (eds.)

ISBN-10: 3540228411

ISBN-13: 9783540228417

This booklet constitutes the lawsuits of the overseas Symposium on Neural N- works (ISNN 2004) held in Dalian, Liaoning, China in the course of August 19–21, 2004. ISNN 2004 obtained over 800 submissions from authors in ?ve continents (Asia, Europe, North the United States, South the USA, and Oceania), and 23 nations and areas (mainland China, Hong Kong, Taiwan, South Korea, Japan, Singapore, India, Iran, Israel, Turkey, H- gary, Poland, Germany, France, Belgium, Spain, united kingdom, united states, Canada, Mexico, Venezuela, Chile, and Australia). in response to reports, this system Committee chosen 329 hello- caliber papers for presentation at ISNN 2004 and booklet within the court cases. The papers are prepared into many topical sections below eleven significant different types (theore- cal research; studying and optimization; help vector machines; blind resource sepa- tion, self reliant part research, and crucial part research; clustering and classi?cation; robotics and regulate; telecommunications; sign, photograph and time sequence processing; detection, diagnostics, and desktop safeguard; biomedical purposes; and different purposes) protecting the total spectrum of the hot neural community learn and improvement. as well as the varied contributed papers, ?ve amazing students have been invited to provide plenary speeches at ISNN 2004. ISNN 2004 used to be an inaugural occasion. It introduced jointly a number of hundred researchers, educators, scientists, and practitioners to the gorgeous coastal urban Dalian in northeastern China.

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Additional resources for Advances in Neural Networks – ISNN 2004: International Symposium on Neural Networks, Dalian, China, August 2004, Proceedings, Part I

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6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. : New Conditions for Global Stability of Neural Networks with Application to Linear and Quadratic Programming Problems, IEEE Trans. Circuits and Systems I. : Exponential Stability and a Systematic Synthesis of a Neural Network for Quadratic Minimization. Neural Networks. : Associative Memory with Non-monntone Dynamics, Neural networks. : Simple “Neural” Optimization Networks: an A/D Converter, Signal Decision Circuit, and a Linear Programming Circuit, IEEE Trans.

Delays only affect convergence rate. However, there are cases that delays play the key role in discussion of whether or not a system is stable. For example, consider the following onedimensional delayed system: x˙ = −x(t − τ ) (2) Its characteristic equation is λ + e−λτ = 0. Therefore, the system (2) is globally asymptotically stable if and only if τ < π2 . Moreover, it was reported in [8], when minimal time lags between inputs and corresponding teacher signal are too long, learning algorithm will take too much time or even not work at all.

Liao, X. X. and Xiao, D. : Globally Exponential Stability of Hopfield Neural Networks with Time-varying Delays. ACTA Electronica Sinica, 28 (2000) 1-4 5. Arik, S. : On the Global Asymptotic Stability of Delayed Cellular Neural Networks. IEEE Trans. Circuits Syst. I, 47 (2000) 571-574 6. Liao, T. L. and Wang, F. : Global Stability for Cellular Neural Networks with Time Delay. IEEE Trans. Neural Networks, 11 (2000) 1481-1484 7. , Heng, P. A. and Leung, K. : Convergence Analysis of Cellular Neural Networks with Unbounded Delay.

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Advances in Neural Networks – ISNN 2004: International Symposium on Neural Networks, Dalian, China, August 2004, Proceedings, Part I by Jianjun Wang, ZongBen Xu, Weijun Xu (auth.), Fu-Liang Yin, Jun Wang, Chengan Guo (eds.)

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