By Chen-Feng Wu, Yu-Teng Chang, Chih-Yao Lo, Han-Sheng Zhuang (auth.), Zhigang Zeng, Jun Wang (eds.)
This publication is part of the complaints of the 7th overseas Symposium on Neural Networks (ISNN 2010), hung on June 6-9, 2010 in Shanghai, China. ISNN 2010 acquired quite a few 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 by means of this system committee contributors and the reviewers, 108 top quality papers have been chosen for guides in Lecture Notes in electric Engineering (LNEE) lawsuits. those papers disguise all significant issues of the engineering designs and functions of neural community study. as well as the contributed papers, the ISNN 2010 technical software incorporated 4 plenary speeches by way of Andrzej Cichocki (RIKEN mind technology Institute, Japan), Chin-Teng Lin (National Chiao Tung college, Taiwan), DeLiang Wang (Ohio country collage, USA), Gary G. Yen (Oklahoma kingdom collage, USA).
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The Harms and Countermeasures of Urban Heat Island Effect (in Chinese). Pollution Control Technology 22(2), 94–96 (2009) 18. : Ecological Measure Analysis of Reducing Urban Heat Island (in Chinese). Ecological Economy (2), 151–153 (2008) 19. : Grey Assessment and Prediction of the Urban Heat Island Effect in Lanzhou City (in Chinese). Journal of Desert Research 29(3), 571–573 (2009) 20. : Grey Assessment and Prediction of the Urban Heat Island Effect in City (in Chinese). Journal of Xi’an Jiaotong University 38(9), 985–988 (2004) 21.
The time series forecasting model of geotechnical engineering is established based on artificial neural network. The input vectors are inputted into the network, using the stable network structure, connection weights and thresholds, so that the samples can be predicted. 3 Catastrophe Cusp Model and Yield Mechanism Cusp theory of the catastrophe theory is the most widely used in the range of mechanics . It has two control variables and a state variable. Its regular function of potential function in the form is as the following: v ( x) = x 4 + px 2 + qx (6) So phase space is three-dimensional, in which x is the state variable, p and q are control variables, and x is the time variable.
But the ANN is hard to decide the structure of the net in the application, and it like slow convergence of learning and liability of dropping into local minimum . The WNN has been a new analysis method of modeling in recent years, which is established with the combination of the wavelet transformation and the traditional ANN thoughts. It is the progression formed by transformation of shifting and dilation with wavelet decomposition, which has the same attribute of approximating function as wavelet decomposition.