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By Chen-Feng Wu, Yu-Teng Chang, Chih-Yao Lo, Han-Sheng Zhuang (auth.), Zhigang Zeng, Jun Wang (eds.)

ISBN-10: 3642129897

ISBN-13: 9783642129896

This publication is part of the lawsuits of the 7th overseas Symposium on Neural Networks (ISNN 2010), hung on June 6-9, 2010 in Shanghai, China. ISNN 2010 obtained various submissions from approximately millions of authors in approximately forty international locations and areas throughout six continents . in line with the rigorous peer-reviews through this system committee contributors and the reviewers, 108 top quality papers have been chosen for guides in Lecture Notes in electric Engineering (LNEE) complaints. those papers conceal all significant themes of the engineering designs and functions of neural community examine. as well as the contributed papers, the ISNN 2010 technical application integrated 4 plenary speeches through Andrzej Cichocki (RIKEN mind technological know-how Institute, Japan), Chin-Teng Lin (National Chiao Tung collage, Taiwan), DeLiang Wang (Ohio country college, USA), Gary G. Yen (Oklahoma country college, USA).

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LNEE 67, pp. 37–45. com © Springer-Verlag Berlin Heidelberg 2010 38 L. Sun, R. Saqi, and H. Xie hyper-parameters into run, implements automatic estimation of ‘nuisance’ parameters, and its kernel function doesn’t need to satisfy ‘Mercer’ condition, its calculate complexity is dramatically reduced and the operating speed is advanced. So the RVM has been applied to much industrial process recently [8,9]. In the paper, we constructs the wavelet relevance vector machine(WRVM) regression prediction model with the wavelet transform and relevance vector machine, and uses to analyze and predicts the heat exchanger fouling increase.

Rock acoustic emission is an intuitive performance of rock micro breakdown, and it is caused by rock crack propagation under load [2]. Acoustic emission method is to predict the occurrence of rock burst based on the parameters in each stage of deformation such as acoustic emission rate, energy rate, and wave shape, and so on, which is the most direct means of detecting and predicting rock burst. Prediction of rock burst is to mainly determine the possibility of rock burst in a large area in the next period[3].

Z. Zeng & J. ): Adv. , LNEE 67, pp. 11–19. com © Springer-Verlag Berlin Heidelberg 2010 12 Y. Xu and D. 1 The Introduction of BP Neural Network Error Back Propagation(BP) neural network is one of the more mature and widely used models. It is a former feed-forward network, mainly composed of input layers, hidden layers and output layers [9]. 1. Fig. 1. BP network topology BP network is a kind of guiding learning network, which relies on adjusting the connection weights between layers of neurons (nodes) to "memory" the learning samples.

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Advances in Neural Network Research and Applications by Chen-Feng Wu, Yu-Teng Chang, Chih-Yao Lo, Han-Sheng Zhuang (auth.), Zhigang Zeng, Jun Wang (eds.)

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