Download e-book for iPad: Advances in Neural Networks – ISNN 2012: 9th International by Alexander A. Frolov, Dušan Húsek, Pavel Yu. Polyakov

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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 lawsuits of the ninth overseas Symposium on Neural Networks, ISNN 2012, held in Shenyang, China, in July 2012. The 147 revised complete papers offered have been conscientiously reviewed and chosen from quite a few submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend popularity; imaginative and prescient; picture processing; details processing; neurocontrol; and novel applications.

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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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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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For the BFA analysis we used the largest genome database KEGG [8], containing the fully sequenced genomes of M = 1368 organisms. LANNIA revealed 38 factors for four full cycles. Each cycle began by running twenty random trajectories in ANNIA. The Lyapunov functions along the eleven true trajectories at the first cycle of LANNIA are shown in Fig. 3(a). At the first cycle, the LM procedure converged for five steps and excluded two factors of eleven. The information gain provided by LM at each its step is shown in Fig.

However, those evolutionary algorithms often suffer from the curse of dimensionality. That is, their performance J. G. M. ): ISNN 2012, Part I, LNCS 7367, pp. 11–20, 2012. © Springer-Verlag Berlin Heidelberg 2012 12 Z. Tang et al. deteriorates quickly as the dimension of the search space increases. In order to improve the efficiency, many researchers have developed hybrid approaches that combine global and local search algorithms [5]. One of the advantages of the hybrid approaches is that evolutionary algorithms can be used to optimize the structure as well as the weights of the neural networks [6].

Each module is a discrete entity of elementary components and performs an identifiable task, separable from the functions of the other modules [7]. Thus, revealing sets of proteins coherently appeared in different organisms may facilitate the search for functional modules in the genome structure. Since the concept of the genome functional modularity is completely compatible with the BFA generative model described here it was a challenge for us to apply LANNIA to reveal the hidden factor structure in some large genome data set.

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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.)


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