Advances in Neural Networks - ISNN 2010: 7th International by Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che,

By Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che, Hongwei Li (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)

This ebook and its sister quantity acquire refereed papers offered on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. construction at the luck of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of well known and top quality meetings on neural computation and its functions. ISNN goals at supplying a platform for scientists, researchers, engineers, in addition to scholars to assemble jointly to offer and talk about the newest progresses in neural networks, and functions in different parts. these days, the sector of neural networks has been fostered some distance past the normal synthetic neural networks. This 12 months, ISNN 2010 obtained 591 submissions from greater than forty nations and areas. in response to rigorous studies, one hundred seventy papers have been chosen for ebook within the complaints. The papers gathered within the lawsuits conceal a vast spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and functions of neural networks. we've geared up the papers into volumes in accordance with their themes. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent issues: neurophysiological starting place, conception and types, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the next 5 issues: SVM and kernel tools, imaginative and prescient and photo, info mining and textual content research, BCI and mind imaging, and applications.

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Extra info for Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II

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Theorem. When each subset has one sample, the polynomial kernel function based on the data vector equals to twice of the polynomial-matrix one based on the autocorrelation matrix. That means the degree d is the twice of the degree D. Proof. When each subset contains one sample, the autocorrelation matrix Σi = xi xT i . Using the polynomial-matrix kernel function, it follows: T T D κ(Σi , Σj ) = ||Σi . ∗ Σi ||D B = ||xi xi . ∗ xj xj ||B ⎛ x2i1 xi1 xi2 ⎜ xi2 xi1 x2i2 ⎜ = || ⎜ . ⎝ .. xin xi1 xin xi2 ⎞ ⎛ 2 xj1 xj1 xj2 .

When artificial intelligence technology is applied to the prediction of time series, the number of input nodes critically affects the prediction performance. According to Kuan [10], this study experimented with the number 4 for the order of autoregressive terms. Thus, 204 observation values became 200 input patterns. The prior 150 input patterns were employed for the training set to build model; the other 50 input patterns were employed for test set to estimate generalization ability of prediction models.

Grid Resource Prediction based on Support Vector Regression and Genetic Algorithms. In: The 5th International Conference on Natural Computation (2009) 13. : Multilayer feedforward networks are universal approximations. Neural Networks, 336–359 (1989) 14. : Learning Internal Representations by Error Propagation in Parallel Distributed Processing. MIT Press, Cambridge (1986) 15. : International and Business Forecasting Methods. cn Abstract. To deal with the computational and storage problem for the large-scale data set, an improved Kernel Principal Component Analysis based on 1-order and 2-order statistical quantity, is proposed.

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