Advances in Neural Networks – ISNN 2007: 4th International - download pdf or read online

By Nan Jiang, Yixian Yang, Xiaomin Ma, Zhaozhi Zhang (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)

ISBN-10: 3540723943

ISBN-13: 9783540723943

ISBN-10: 3540723951

ISBN-13: 9783540723950

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 offered have been rigorously reviewed and chosen from a complete of 1,975 submissions. The papers are geared up in topical sections on neural fuzzy regulate, neural networks for keep watch over functions, adaptive dynamic programming and reinforcement studying, neural networks for nonlinear platforms 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 buildings, neural networks for trend acceptance, SOMs, ICA/PCA, biomedical purposes, feedforward neural networks, recurrent neural networks, neural networks for optimization, help vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and functions of neural networks.

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Extra resources for Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part III

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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). 21 (2005) 230–232 16 H. Li, H. Li, and Y. Du 4. : Multilayer Feed Forward Networks Are Universal Approximators.

Thus, there are 13 neurons in input vectors of damage extent detection. {output}={o1 o2 … o10} 0 oi 1 indicates the ith element damage extent. The neural networks were trained by element damage with elastic modulus discount5%, 20%, 50%, and meanwhile, in order to extend the training patterns, there are 5% random noises added to the nodes displacements. The neural networks were tested with elastic modulus discount 30%, 70%. 4, at the range of limited level noises, the noises have little effect on the identification errors, the identification accuracy depend on damage extent adopted during testing.

Step 3. W1 = W1∗ + δe1 (δ > 0) . e1 is the first unit vector, and A > 0. Step 4. Search from the point W1 to find the minimal point Wk∗ of H(W, A) with QN method. Step 5. Make W = Wk∗ , if the any one of criterion below is satisfied: ∗ , A), (2) H(Wk∗ , A) ≥ 0, (3) dTk−1 ∇H(Wk∗ , A) ≥ 0, (1) H(Wk∗ , A) ≥ H(Wk−1 ∗ ∗ T ∗ (4) (Wk − W1 ) ∇H(Wk , A) ≥ 0, (5) ∇H(Wk∗ , A) ≤ ε(εis the positive integer). Step 6. Optimize the E(W ) from the new minimal point W , then we could get the another minimal point W2∗ .

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Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part III by Nan Jiang, Yixian Yang, Xiaomin Ma, Zhaozhi Zhang (auth.), Derong Liu, Shumin Fei, Zengguang Hou, Huaguang Zhang, Changyin Sun (eds.)


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