By Jianjun Wang, ZongBen Xu, Weijun Xu (auth.), Fu-Liang Yin, Jun Wang, Chengan Guo (eds.)
This e-book 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 got over 800 submissions from authors in ?ve continents (Asia, Europe, North the United States, South the United States, 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 experiences, this system Committee chosen 329 hello- caliber papers for presentation at ISNN 2004 and e-book within the complaints. The papers are geared up into many topical sections below eleven significant different types (theore- cal research; studying and optimization; help vector machines; blind resource sepa- tion, self sustaining part research, and critical part research; clustering and classi?cation; robotics and keep an eye on; telecommunications; sign, snapshot and time sequence processing; detection, diagnostics, and machine safeguard; biomedical purposes; and different purposes) overlaying the total spectrum of the hot neural community examine and improvement. as well as the varied contributed papers, ?ve extraordinary students have been invited to provide plenary speeches at ISNN 2004. ISNN 2004 was once an inaugural occasion. It introduced jointly a couple 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
R. China, 430074. Abstract. Using the Razumikhin-type theory and matrix analysis method, A suﬃcient criterion about globally asymptotic stability of the neural networks is obtained. , and various results were reported (see [1-9]). However, in hardware implementation, uncertainty and time delays occur due to disturbance between the electric components and ﬁnite switching speeds of the ampliﬁers, can aﬀect the stability of a network by creating oscillatory and unstable characteristics. It is important to investigate the dynamics of uncertain neural networks with time delays.
Under the Orthogonal polynomials basis and certain assumptions of activation functions in the neural network, the upper bounds on the degree of approximation are obtained in the class of funcr tions considered in this paper. The order of approximation O(n− d ), d being dimension, n the number of hidden neurons, and r the natural number. Chen . Their work concentrated on the question of denseness. But from the point of application, we are concerned about the degree of approximation by neural networks.
Id }. 0≤|i|≤|m| Hence, we have the following theorem. r,d Theorem 1. For 1 ≤ p < ∞, let f ∈ Ψp,ω . , md ), mi ≤ m, we have inf f − p p,ω ≤ Cm−r . p∈Pm Proof. We consider the Chebyshev orthogonal polynomials Tm (x), and obtain the following equality from (6) mi Vi,mi (f ) = ξs fs,i Ts (xi ), s=1 where fs,i = tors [−1,1]d f (x)Ts (xi )ω(xi )dxi . Hence, we deﬁne the following opera- V (f ) = V1,m1 V2,m2 · · · Vd,md f md m1 ··· = s1 =1 where fs1 ,... ,sd = [−1,1]d f − V (f ) ξs1 · · · ξsd fs1 ,...