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 court cases 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 awarded have been conscientiously 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 purposes, 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 development attractiveness, SOMs, ICA/PCA, biomedical purposes, feedforward neural networks, recurrent neural networks, neural networks for optimization, aid vector machines, fault diagnosis/detection, communications and sign processing, image/video processing, and functions of neural networks.
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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
277 suffers are chosen as the training samples and 145 ones as checking samples. Four algorithms are adopted, including gd, bfg, rp and lm to approximate the nonlinear relationship of the NIHSS and TCM-SSASD. For the multiple models scheme 1, one 24 Y. Zhang et al. thousand models are used to build an average one. The result can be seen in the following table 2. It is shown that one hidden layer with 10 neurons is sufficient enough to approximate the nonlinear relationship. The LM algorithms bring forth a better performance and quickly convergence speed.
The sources of noise include instrumental noise and environmental noise. This work takes noise into account in order to study the noise tolerance ability of the proposed approach. In this example, 240 sets of noise-contained testing data are generated by adding ±10%, ±20%, and ±30%, respectively, of randomly distributed noise into the training data to test the BPNN diagnosis system. Table 2 shows the test results with different percentage of noise. As shown in Table 2, the descending of recognition rate generally follows the raising of noise percentage, a finding that is consistent with general expectations.
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