By Shuyan Chen, Wei Wang (auth.), Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin (eds.)
This ebook and its sister volumes represent the court cases of the 3rd overseas Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China in the course of could 28–31, 2006. After a winning ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN grew to become a well-established sequence of meetings on neural computation within the area with turning out to be reputation and bettering caliber. ISNN 2006 got 2472 submissions from authors in forty three nations and areas (mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, Malaysia, India, Pakistan, Iran, Qatar, Turkey, Greece, Romania, Lithuania, Slovakia, Poland, Finland, Norway, Sweden, Demark, Germany, France, Spain, Portugal, Belgium, Netherlands, united kingdom, eire, Canada, united states, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) throughout six continents (Asia, Europe, North the US, South the United States, Africa, and Oceania). in keeping with rigorous reports, 616 high quality papers have been chosen for booklet within the lawsuits with the reputation price being under 25%. The papers are geared up in 27 cohesive sections protecting all significant issues of neural community examine and improvement. as well as the varied contributed papers, ten exclusive students gave plenary speeches (Robert J. Marks II, Erkki Oja, Marios M. Polycarpou, Donald C. Wunsch II, Zongben Xu, and Bo Zhang) and tutorials (Walter J. Freeman, Derong Liu, Paul J. Werbos, and Jacek M. Zurada).
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Additional resources for Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part III
However, the value may be different for different cases. This should be test in experiments. STEP 5: Repeat STEP 2 to Step 4 until all clusters are investigated. Actually, ambiguity exists here in . That is, after a cycle ended, user may restart a new cycle in order to merge more clusters till the number of clusters doesn’t change any more. That’s reasonable; however, experiments show more cycles may rapidly decrease the number of clusters, which would subsequently affect the performance of the classifier.
The results show that LS-SVM outperforms BP neural network in the prediction of railway passenger traffic volume. The rest of this paper is organized in the following manner. Section 2 describes regression LS-SVM. Section 3 performs the prediction task of railway passenger traffic volume. Section 4, the last part, concludes this work. 2 Regression LS-SVM The basic idea of regression SVM is to nonlinearly map the training data via mapping function into a higher dimensional feature space, and then obtain a linear regression problem and solve it in this feature space .
Wang Figure 2 illustrated the real data in test set and its prediction value, yield by the hybrid method and general BPNNs approach with the best performance over the test data corresponding to No 4 in table 1. As this plot indicated, the hybrid method gave a better goodness of fit. 400 Original Hybrid traffic volume 300 200 100 0 20 30 40 50 60 70 80 (a) real traffic volume and it prediction by the hybrid approach 90 100 400 Original General traffic volume 300 200 100 0 20 30 40 50 60 70 80 (b) real traffic volume and it prediction by the general BPNNs 90 100 Fig.