Advances in Neural Networks - ISNN 2006: Third International by Shuyan Chen, Wei Wang (auth.), Jun Wang, Zhang Yi, Jacek M.

By Shuyan Chen, Wei Wang (auth.), Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin (eds.)

This e-book and its sister volumes represent the complaints of the 3rd overseas Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China in the course of may well 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 quarter with turning out to be attractiveness and enhancing caliber. ISNN 2006 got 2472 submissions from authors in forty three international locations 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 United States, South the USA, Africa, and Oceania). in keeping with rigorous stories, 616 top of the range papers have been chosen for booklet within the lawsuits with the popularity fee being lower than 25%. The papers are geared up in 27 cohesive sections protecting all significant subject matters of neural community study and improvement. as well as the varied contributed papers, ten distinctive 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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Extra info for Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part III

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The Application of Improved BP Neural Network in the Prediction of Railway Passenger Volume Time Serial. China Railway Science 26(2) (2005) 128-131 2. : Analysis and Evaluation of Five Short-Term Load Forecasting Techniques. IEEE Trans. Power Syst. 4(4) (1989) 1484-1491 3. : A Gray System Modeling Approach to the Prediction of Calibration Intervals. IEEE Trans. Instrum. Meas. 54(1) (2005) 297-304 4. : Neural Networks for Short-Term Load Forecasting: A Review and Evaluation. IEEE Trans. Power Syst.

IEEE Intelligent Vehicles Symposium (2004) 188-193 4. : Implementation of an RBF Neural Network on Embedded Systems: Real-Time Face Tracking and Identity Verification. IEEE Transactions on Neural Networks 14(5) (2003) 1162-1175 5. : Image Redundancy Reduction for Neural Network Classification using Discrete Cosine Transforms. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (2000) 149-154 6. : High-Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks.

The prediction results by LS-SVM model are compared with those by BP nerual network. 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 [7].

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