Get Advances in Neural Networks - ISNN 2010: 7th International PDF

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

ISBN-10: 3642132774

ISBN-13: 9783642132773

ISBN-10: 3642133177

ISBN-13: 9783642133176

This publication 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 good fortune of the former six successive ISNN symposiums, ISNN has turn into a well-established sequence of renowned and fine quality meetings on neural computation and its purposes. ISNN goals at delivering a platform for scientists, researchers, engineers, in addition to scholars to assemble jointly to give and talk about the newest progresses in neural networks, and purposes in different parts. these days, the sector of neural networks has been fostered some distance past the normal synthetic neural networks. This yr, ISNN 2010 got 591 submissions from greater than forty international locations and areas. in line with rigorous studies, one hundred seventy papers have been chosen for e-book within the complaints. The papers gathered within the court cases hide a extensive spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and purposes of neural networks. we've got geared up the papers into volumes in line with their themes. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent issues: neurophysiological starting place, concept and versions, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the next 5 subject matters: SVM and kernel equipment, imaginative and prescient and picture, info mining and textual content research, BCI and mind imaging, and applications.

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Read Online or Download Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II PDF

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Additional resources 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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Considering precision and computing time, we picked 30 discrete points equally from the searching space of C, 30 from σ and 20 from ε. Hence, we got 18000 (18000= 30 × 30 × 20) group of parameters. Cross validation technique was also applied to trialand-error procedure. The optimal parameters that provided the smallest MSEcv on the training set were obtained after each group of parameters was tried. 3) BPNN model In the area of time series prediction, the most popular ANN model is the BPNN due to its simple architecture yet powerful problem-solving ability.

The details of ACO-SVR model are described as follows: (1) Path Selection: Each influencing factor in current system is regarded as a city node. An ant n in city a chooses the next city b to move to by applying the following probabilistic formula: if q < Q , else ⎧ arg max{ τ t ab }, T (a, b) = ⎨ Sr ⎩ 0 (9) where q is a variable which is chosen randomly with uniform probability [0,1], Q0 ∈ (0,1) is a parameter and τ represents pheromone. Sr means that a standard roulette wheel selection is employed to determine the next city.

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.

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Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II by Guosheng Hu, Liang Hu, Jing Song, Pengchao Li, Xilong Che, Hongwei Li (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)

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