By Nan Jiang, Yixian Yang, Xiaomin Ma, Zhaozhi Zhang (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)
This publication is a part of a 3 quantity set that constitutes the refereed complaints of the 4th overseas Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007.
The 262 revised lengthy papers and 192 revised brief papers provided have been rigorously reviewed and chosen from a complete of 1,975 submissions. The papers are equipped in topical sections on neural fuzzy keep watch over, neural networks for keep watch over functions, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear structures modeling, robotics, balance research of neural networks, studying and approximation, info mining and have extraction, chaos and synchronization, neural fuzzy structures, education and studying algorithms for neural networks, neural community constructions, neural networks for trend reputation, SOMs, ICA/PCA, biomedical functions, feedforward neural networks, recurrent neural networks, neural networks for optimization, aid vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and purposes of neural networks.
Read or Download Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part III PDF
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The expanding complexity of telecommunication networks calls for us to increase enough mathematical versions. We needs to locate their features, optimize them topic to selected standards, and enhance the corresponding keep watch over algorithms. Multidimensional queueing types are used to layout and optimize glossy and next-generation networks (NGN).
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Additional info for Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part III
Journal of Harbin University of Commerce(Natural Sciences Edition). 21 (2005) 230–232 16 H. Li, H. Li, and Y. Du 4. : Multilayer Feed Forward Networks Are Universal Approximators. Neural Networks. 2 (1989) 359–366 5. Andersen R. : Properties of the Random Search in Global Optimization. Journal of Optimization Theory and Applications. 16 (1975) 383–398 6. David J. , Frenzel J. : Training Product Unit Neural Networks with Genetic Algorithms. IEEE Expert. 8 (1993) 26–33 7. , Gelatt C. , Vecchi M. : Optimization by Simulated Annealing.
1. The ﬁlled-function of the two-dimension curvilinear function F When we got the minimal point X1 on f (X), a new Filled-Function model on the point X1 could be constructed as follows: H(X, A) = 1 1+ 2 F (X) − F (X1 ) − A ln(1 + X − X1 )(A > 0) (2) Before put this Filled-Function model into an algorithm, we must prove that this one could satisfy the basic quality of the Filled-Function. Proposition 1. If X1 is the minimal point, whatever the positive integer A would be, X1 is the maximal point on the H(X, A).
For the problem of adjusting the important parameter A in a more convincing method, the writers are doing some more researches now. References 1. : A New Global Optimization Algorithm for Training Feedforward Neural Networks and Its Application. System Engineering Theory and Practice. 8 (2003) 42–47 2. : A New Global Optimization BP Neural Networks. Jouenal of Binzhou Teachers College. 20 (2004) 37–41 3. : Study on Class of Filled-Functions for Global Optimization. Journal of Harbin University of Commerce(Natural Sciences Edition).
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