By Jianjun Wang, ZongBen Xu, Weijun Xu (auth.), Fu-Liang Yin, Jun Wang, Chengan Guo (eds.)
This booklet constitutes the lawsuits of the foreign 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 USA, South the US, and Oceania), and 23 international locations 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 experiences, this system Committee chosen 329 hello- caliber papers for presentation at ISNN 2004 and booklet within the court cases. The papers are geared up into many topical sections lower than eleven significant different types (theore- cal research; studying and optimization; aid vector machines; blind resource sepa- tion, self reliant part research, and imperative part research; clustering and classi?cation; robotics and keep an eye on; telecommunications; sign, photo and time sequence processing; detection, diagnostics, and computing device safeguard; biomedical purposes; and different purposes) overlaying the entire spectrum of the hot neural community examine and improvement. as well as the various contributed papers, ?ve special students have been invited to offer plenary speeches at ISNN 2004. ISNN 2004 was once an inaugural occasion. It introduced jointly a number of hundred researchers, educators, scientists, and practitioners to the attractive 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
1 can be called “Nonlinearity Spectrum”, which reveals two sets of information. First, for a given function, how many nonlinearity components could be generated by changing the free parameters. Each component represents a unique class of nonlinear functions, say, N Cj for the jth nonlinearity component, which could be an “S-type curve”. Second, for each included component, what is its associated nonlinearity variation range. This range exhibits the admissible range realized by the given function.
Otherwise, it is a linear parameter (also called location parameter). Deﬁnition 2. Nonlinearity domain, nonlinearity components and nonlinearity variation range. Nonlinearity domain is a two dimensional space used for characterization of nonlinearity of functions. Nonlinearity components are a set of discrete points with Geometric Interpretation of Nonlinear Approximation Capability 9 an inﬁnite number along the horizontal axis of nonlinearity domain. The vertical axis represents the variable of nonlinearity variation range.
Picking B 1 = 0 and B 3 = 0, we have Corollary 2. such that If gj (·), fj (·) satisfy H, j = 1, · · · , n, and there exist ξi > 0 n + − di ξi + aii ξi + |aji |ξj j=i |bji |ξj Fi < 0 Gi + i = 1, · · · , n (7) j=1 Then equilibrium u∗ is globally exponentially asymptotically stable. Remark 2. It can be seen that condition (7) is independent of delays, and results of [1,2,3,4,5] are all certain forms of Corollary 2. 3 Numerical Illustration In this section, we present a delayed system, which is stable for small delays.