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

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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 e-book and its sister quantity gather refereed papers offered on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. development at the good fortune of the former six successive ISNN symposiums, ISNN has develop into a well-established sequence of renowned and high quality meetings on neural computation and its functions. ISNN goals at delivering 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 purposes in varied components. these days, the sphere of neural networks has been fostered a long way past the normal synthetic neural networks. This yr, ISNN 2010 obtained 591 submissions from greater than forty international locations and areas. in response to rigorous experiences, one hundred seventy papers have been chosen for e-book within the complaints. The papers gathered within the court cases disguise a vast spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and purposes of neural networks. we've got equipped the papers into volumes in line with their subject matters. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the next issues: neurophysiological origin, thought 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 tools, imaginative and prescient and photograph, information 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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The simulation of SVR model had been carried out by using the ‘Libsvm’, a toolbox for support vector machines, which was originally designed by Chang and Lin [11]. 79GHz and 2 GB RAM. Some statistical metrics, such as NMSE and R, were used to evaluate the prediction performance of models [12]. 2 Parameters Determination for Three Models 1) ACO-SVR model The choices of ACO’s parameters were based on numerous experiments, as those values provided the smallest MSEcv on the training data set. Table 1 gave an overview of ACO parameter settings.

Weigen Wu, Jimin Yuan, Jun Li, Qianrong Tan, and Xing Yin 745 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . com Abstract. Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resources prediction. In order to build an effective SVR model, SVR’s parameters must be selected carefully. Therefore, we develop an ant colony optimization-based SVR (ACO-SVR) model that can automatically determine the optimal parameters of SVR with higher predictive accuracy and generalization ability simultaneously.

In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resources prediction. In order to build an effective SVR model, SVR’s parameters must be selected carefully. Therefore, we develop an ant colony optimization-based SVR (ACO-SVR) model that can automatically determine the optimal parameters of SVR with higher predictive accuracy and generalization ability simultaneously. The proposed model was tested with grid resources benchmark data set.

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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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