By Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)

ISBN-10: 3642313450

ISBN-13: 9783642313455

ISBN-10: 3642313469

ISBN-13: 9783642313462

ISBN-10: 3642313612

ISBN-13: 9783642313615

ISBN-10: 3642313620

ISBN-13: 9783642313622

The two-volume set LNCS 7367 and 7368 constitutes the refereed complaints of the ninth overseas Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers awarded have been rigorously reviewed and chosen from a number of submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; development acceptance; imaginative and prescient; snapshot processing; details processing; neurocontrol; and novel applications.

**Read or Download Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I PDF**

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**Extra resources for Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I**

**Example text**

G. M. ): ISNN 2012, Part I, LNCS 7367, pp. 29–36, 2012. -J. Zhao, J. Tang, and T. Chai machines (SVM). However, the learning speed of SVM is slow and the model parameters are very sensitive to the prediction performance. The least square-support vector machines (LS-SVM) can simplify the quadratic program (QP) problem of the SVM to solve a set of linear equations in the sense of sub optimum. Although the learning speed is improved, the sparse of the SVM is lost, and the sensitivity problem of the models’ learning parameters is not solved.

Tang, and T. Chai machines (SVM). However, the learning speed of SVM is slow and the model parameters are very sensitive to the prediction performance. The least square-support vector machines (LS-SVM) can simplify the quadratic program (QP) problem of the SVM to solve a set of linear equations in the sense of sub optimum. Although the learning speed is improved, the sparse of the SVM is lost, and the sensitivity problem of the models’ learning parameters is not solved. The extreme leaning machines (ELM)[7] obtained better learning speed than SVM by using the minimal norm least square method instead of the standard optimization method.

5. Prediction results of the level of saccharose of an orange juice model Table 1. 3775 The prediction results show that the MI-KELM algorithm has the fastest learning speed compare with the normal KELM approaches. With only half of the origin input features, the prediction accuracy is almost same with KELM model based on the full spectrum with lower complexity. The interpretation of the selected wavelength would be further addressed. With the extreme learning speed, this approach is useful in the online update the models.

### Advances in Neural Networks – ISNN 2012: 9th International Symposium on Neural Networks, Shenyang, China, July 11-14, 2012. Proceedings, Part I by Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov (auth.), Jun Wang, Gary G. Yen, Marios M. Polycarpou (eds.)

by Daniel

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