By Jianjun Wang, ZongBen Xu, Weijun Xu (auth.), Fu-Liang Yin, Jun Wang, Chengan Guo (eds.)
This e-book constitutes the court cases 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 acquired 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). according to studies, this system Committee chosen 329 hello- caliber papers for presentation at ISNN 2004 and booklet within the lawsuits. The papers are equipped into many topical sections less than eleven significant different types (theore- cal research; studying and optimization; help vector machines; blind resource sepa- tion, self sustaining part research, and vital part research; clustering and classi?cation; robotics and keep an eye on; telecommunications; sign, picture and time sequence processing; detection, diagnostics, and laptop protection; biomedical purposes; and different functions) protecting the full spectrum of the new neural community learn and improvement. as well as the varied contributed papers, ?ve unusual students have been invited to provide plenary speeches at ISNN 2004. ISNN 2004 used to be 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
904 Chun Jin, Xinlu Liu, Peng Gao A Freeway Traffic Incident Detection Algorithm Based on Neural Networks . . . . . . . . . . . . . . . . . . . . . . 912 Xuhua Yang, Zonghai Sun, Youxian Sun Radial Basis Function Network for Chaos Series Prediction . . . . . . . . . 920 Wei Chi, Bo Zhou, Aiguo Shi, Feng Cai, Yongsheng Zhang Feature Extraction and Identification of Underground Nuclear Explosion and Natural Earthquake Based on FMm let Transform and BP Neural Network .
Sigmoidal, Gaussian, and Chebyshev-polynomials. Their mathematic representations are given in the following forms. The simplest sigmoidal FNNs: y= a +d 1 + exp(bx + c) (3) The simplest Gaussian FNNs: y = a exp − (x − c)2 +d b2 (4) Geometric Interpretation of Nonlinear Approximation Capability 11 The simplest Chebyshev-polynomial FNNs: y = aT3 (x) + bT2 (x) + cT1 (x) + d (5) where Ti (x) are the Chebyshev polynomials (see  and their architectures of FNNs). Both simplest sigmoidal and Gaussian FFNs apply at most four free parameters.
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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.)