Download PDF by Nan Jiang, Yixian Yang, Xiaomin Ma, Zhaozhi Zhang (auth.),: Advances in Neural Networks – ISNN 2007: 4th International

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 ebook is a part of a 3 quantity set that constitutes the refereed court cases of the 4th foreign 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 conscientiously reviewed and chosen from a complete of 1,975 submissions. The papers are equipped in topical sections on neural fuzzy regulate, 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 platforms, education and studying algorithms for neural networks, neural community constructions, neural networks for development acceptance, 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.

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Extra 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

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Acta Electronica Sinica (1996) 1-6 8 N. Jiang et al. 3. : Capacity of Multilevel Threshold Devices. IEEE Transactions on Information Theory (1998) 241-255 4. : On Encoding and Enumerating Threshold Functions. IEEE Transactions on Neural Networks (2004) 261-267 5. : Enumeration of Linear Threshold Functions from the Lattice of Hyperplane Intersections. IEEE Transactions on Neural Networks (2000) 839-850 6. : STRIP --- a Strip-based Neural-Network Growth Algorithm for Learning Multiple-valued Functions.

Step 8. g. A = 2A, and turn to Step 2. For the problem of adjusting the important parameter A: In FilledFunction, A is a very important parameter. From the Proposition 2, when E(W ) rises, H(W, A) would descend as long as A is a positive integer. From the Proposition 3, when A > A∗ and E(W ) descend, H(W, A) could still descend along the direction d. From the Proposition 4, in the basin lower than B1 , when E(W ) descends, if A < A∗ , then H(W, A) would rise along direction d. It indicates that only if the parameter A is in a limited range, H(W, A) would be an available 14 H.

Chen proposed approach is considered a general tool because it can be easy implemented on the popular MATLAB software. Moreover, the approach is considered a flexible tool because it can be also applied to other high-voltage power apparatuses such as current transformer (CT), potential transformer (PT), cable, and rotation machine. Experimental results indicate that the proposed approach has a high degree of recognition accuracy and good tolerance of noise interference. We expect this work providing useful reference to electric power industry.

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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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