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 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 nations and areas throughout six continents . according to the rigorous peer-reviews via this system committee individuals and the reviewers, 108 fine quality papers have been chosen for guides in Lecture Notes in electric Engineering (LNEE) court cases. those papers hide all significant subject matters of the engineering designs and functions of neural community learn. as well as the contributed papers, the ISNN 2010 technical application integrated 4 plenary speeches by means 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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Extra resources for Advances in Neural Network Research and Applications
It was superior to results of SCGM model and GM(1,1) model. The result shows that backpropagation neural network is effective to predict urban heat island intensity. ℃ Keywords: Backpropagation neural network, Prediction, Urban heat Island intensity, Chuxiong. 1 Introduction An urban heat island (UHI) is an urban area which is significantly warmer than its surrounding rural areas . Urban heat island is one of the most important features of urban climate and atmospheric environment, urban human factors and local meteorological condition cause urban heat island to form .
Fig. 2. An example of updating the pattern list The process of updating the pattern list is illustrated in Fig. 2. At time t+1, the actual time series data X(t+1) that is with former pattern [1, 2, 4, -3] is received and transferred into G(t+1) by pre-processing process. If the actual value of G(t+1) is -2, 1, or 6, the count value of the existing pattern list will increase, and the corresponding confident level will be risen at next prediction. A new pattern will be added to the pattern list, if G(t+1) is not an expected value, such as 8.
Table 1 was the processing results. Table 1. 7280 32 W. Xi and P. He Table 1. 8947 Prediction of Urban Heat Island Intensity in Chuxiong City 33 Table 1. 0175 Data source: Chuxiong city yearbook (1994-2007), Lucheng town statistical station. Prediction with backpropagation neural network. Analysis tool was DPS Data processing system , it provided a rapid way to compute with backpropagation neural network. First, we imported all non-dimensional quantities into DPS Data processing system, then made backpropagation neural network analyze with 9 influencing factors and urban heat island intensity from 1981 to 2005.
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.)