Download Advances in Neural Networks – ISNN 2012: 9th International by Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov PDF

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

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 provided have been conscientiously reviewed and chosen from a variety of submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; development reputation; imaginative and prescient; picture processing; details processing; neurocontrol; and novel applications.

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

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References 1. : Learning internal representations by error propagation. In: Parallel Distributed Processing: Exploration in the Microstructure of Cognition, pp. 318–362. MIT Press, Cambridge (1986) 2. : Optimization and global minimization methods suitable for neural networks. Neural Computing Surveys 2, 163–212 (1999) 3. : Evolving artificial neural networks. Proceedings of the IEEE 89(9), 1423–1447 (1999) 4. : Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, Perth, Australia, Piscataway, NJ, pp.

G. M. ): ISNN 2012, Part I, LNCS 7367, pp. 21–28, 2012. -D. -K. -K. Kim The three types of polynomials are considered as connection weight such as linear, quadratic and modified quadratic. The coefficients of polynomial are obtained by Weighted Least Square Estimation. Section 2 describes the architecture of the context FCM-based RBF NNs and section 3 presents a learning method applied to the construction of proposed model. Section 4 presents the experimental results. Finally, some conclusions are drawn in Section 5.

3 and Fig. 4. It shows that the robustness of the cost parameters C in kernel based ELM model is better. The comparison among different threshold and method is shown Fig. 5, and the statistical results are shown in Table 1. Fig. 3. Performance of kernel ELM based on full spectrum Fig. 4. 3500 Modeling Spectral Data Based on MI and Kernel Extreme Learning Machines 35 Fig. 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.

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