By Jianjun Wang, ZongBen Xu, Weijun Xu (auth.), Fu-Liang Yin, Jun Wang, Chengan Guo (eds.)
This booklet constitutes the court cases 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 got over 800 submissions from authors in ?ve continents (Asia, Europe, North the USA, 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 line with stories, 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 less than eleven significant different types (theore- cal research; studying and optimization; aid vector machines; blind resource sepa- tion, self reliant part research, and vital part research; clustering and classi?cation; robotics and regulate; telecommunications; sign, photograph and time sequence processing; detection, diagnostics, and laptop defense; biomedical purposes; and different purposes) overlaying the total spectrum of the new neural community examine and improvement. as well as the varied contributed papers, ?ve uncommon 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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Extra info for Advances in Neural Networks – ISNN 2004: International Symposium on Neural Networks, Dalian, China, August 2004, Proceedings, Part I
799 Jinsong Gui, Hequan Sun, Haigui Kang Modeling Temperature Drift of FOG by Improved BP Algorithm and by Gauss-Newton Algorithm . . . . . . . . . . . . . . . . . . . 805 Xiyuan Chen A Novel Chaotic Neural Network for Automatic Material Ratio System . . . . 813 Lidan Wang, Shukai Duan Finite Element Analysis of Structures Based on Linear Saturated System Model . . . . . . . . . . . . . . . . 820 Hai-Bin Li, Hong-Zhong Huang, Ming-Yang Zhao Neural Network Based Fatigue Cracks Evolution .
Although these two components are special cases for the zero degree of nonlinearity, both of them cannot be missed for the completeness of nonlinearity components. The next will start from simple nonlinear curves, say, “C-type” and “S-type” curves. We will give more detailed classiﬁcation examples later. , 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).
779 Zeying Yang, Chengkui Huang, Jianbo Qu Determining of the Delay Time for a Heating Ventilating and Air-Conditioning Plant Using Two Weighted Neural Network Approach . . 786 Mengdi Hu, Wenming Cao, Shoujue Wang Intelligent Forecast Procedures for Slope Stability with Evolutionary Artificial Neural Network . . . . . . . . . . . . . . . 792 Shouju Li, Yingxi Liu Structural Reliability Analysis via Global Response Surface Method of BP Neural Network .