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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 ebook is part of the lawsuits of the 7th foreign Symposium on Neural Networks (ISNN 2010), hung on June 6-9, 2010 in Shanghai, China. ISNN 2010 got various submissions from approximately hundreds of thousands of authors in approximately forty international locations and areas throughout six continents . in keeping with the rigorous peer-reviews through this system committee participants and the reviewers, 108 top quality papers have been chosen for courses in Lecture Notes in electric Engineering (LNEE) lawsuits. 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 incorporated 4 plenary speeches by way of Andrzej Cichocki (RIKEN mind technological know-how Institute, Japan), Chin-Teng Lin (National Chiao Tung collage, Taiwan), DeLiang Wang (Ohio nation college, USA), Gary G. Yen (Oklahoma country college, USA).

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The altitude is between 1700 meters to 2300 meters. 6 , and average annual rainfall is 864 millimeters. Chuxiong belongs to subtropical monsoon climate [22]. 2 Sequential Variation of Urban Heat Island in Chuxiong City According to the professor Ping He's research [6], in the years prior to 1988, the urban heat island intensity in Chuxiong city was below 1 , since then, the intensity was going up over 1 . It increased quickly and reached 2 in 2006 (Figure 2). ℃ ℃ ℃ Fig. 2. Variation of annual heat island intensity in Chuxiong city from 1959 to 2000 The maximum value of heat island intensity in Chuxiong city from 1980 to 2000 happened in spring (from March to April), the minimum happened in summer (from June to July) (Figure 3).

26 H. Niu et al. Fig. 3. The fitting chart between the true value and the prediction value of BP Fig. 4. The fitting chart between the true value and the prediction value of WNN Application of Wavelet Neural Network to Prediction of Water Content in Crude Oil 27 Table 1. 0 10. 0562 5 Conclusion This paper proposed the prediction model of water content in crude oil based on wavelet neural network, use the nonlinear wavelet basis replace the excitation function of the neuron in neural network. It combines advantage of the wavelet and the traditional neural network, and through the calculation function such as flexible and shifting to multi-scale analysis the data.

And then, inserting a i in Formula 13 to Formula 15, we can get the value of p and q , which can be inserted in Formula 10 to calculate the value of F. At last, we can forecast the rock burst circumstances based on the value of F. 4 Application of BP Neural Network – Mutation Model in Rock Burst Prediction of Acoustic Emission To verify the accuracy of the neural network - mutation model in rock burst prediction, we use the data recorded on the experimental to test the model. The white marble specimens, whose integrity is good, are used for testing.

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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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