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 court cases of the ninth foreign 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 variety of submissions. The contributions are based in topical sections on mathematical modeling; neurodynamics; cognitive neuroscience; studying algorithms; optimization; trend popularity; imaginative and prescient; photo processing; details processing; neurocontrol; and novel applications.

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Additional info 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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Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes. IEEE Transactions on Neural Networks 17, 879–892 (2006) 11. : Convex Incremental Extreme Learning Machine. Neurocomputing 70, 3056–3062 (2007) 12. : Enhanced Random Search Based Incremental Extreme Learning Machine. edu 2 MathWorks, Inc. com Abstract. In this paper, a hierarchical neural network with cascading architecture is proposed and its application to classification is analyzed. This cascading architecture consists of multiple levels of neural network structure, in which the outputs of the hidden neurons in the higher hierarchical level are treated as an equivalent input data to the input neurons at the lower hierarchical level.

One Hidden Layer with a Single Output For a single-output FNN with one hidden layer, the output of the network is  h    o = f ( w, x) = f   w j f j   wij x ij + w0 j  + w0   i   j  14 Z. Tang et al. where h is the number of hidden units, and fj’s are activation functions in the hidden layer. Note that the output o can be written as a composite function o = f(g(w, x)), where h   g =  w j f j   wij x ij + w0 j  + w0 . 1, we have Lo = Lf Lg. Lf is given by L f = max || ∇ g f ( w, x ) || = max γ f (1 − f ), ∀w ∈ W where γ is the learning rate.

The information gain provided by LM at each its step is shown in Fig. 3(b). 27. At the second full LANNIA cycle, ANNIA found fourteen factors. 31. At the third and fourth full LANNIA cycles, ANNIA found thirteen and twelve factors, LM excluded six and three. At fifth cycle, the gain decreased and LANNIA was terminated. 32. The high gain obtained shows that, first, the genome data are actually adequate for BFA and, second, LANNIA provides the good BFA performance. The high information gain speeds in favor of the hypothesis of the modular genome structure [7].

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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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