Advances in Neural Networks – ISNN 2009: 6th International - download pdf or read online

By Mingchang Li, Guangyu Zhang, Bin Zhou, Shuxiu Liang, Zhaochen Sun (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)

ISBN-10: 3642015069

ISBN-13: 9783642015069

ISBN-10: 3642015077

ISBN-13: 9783642015076

The 3 quantity set LNCS 5551/5552/5553 constitutes the refereed lawsuits of the sixth foreign Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in could 2009.

The 409 revised papers offered have been rigorously reviewed and chosen from a complete of 1.235 submissions. The papers are equipped in 20 topical sections on theoretical research, balance, time-delay neural networks, laptop studying, neural modeling, determination making structures, fuzzy structures and fuzzy neural networks, help vector machines and kernel equipment, genetic algorithms, clustering and type, trend attractiveness, clever keep an eye on, optimization, robotics, photograph processing, sign processing, biomedical functions, fault prognosis, telecommunication, sensor community and transportation platforms, in addition to applications.

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Read or Download Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part I PDF

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Additional info for Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part I

Example text

6) 14 Y. Zhang, Y. Yang, and N. Tan In addition, by using the nonlinear-activation technique [1,2,4,6,11], we could have the following generalized nonlinear GNN model of (6): ˙ X(t) = −γX T (t)F X 2 (t) − A − γF X 2 (t) − A X T (t). , the square roots finding with constant matrix A ∈ Rn×n in (1)] and could only have exact convergence results for the situation of static problems solving. Moreover, before ending this section, we would like to present the following important remarks about the comparison between ZNN (4) and GNN (6).

Springer-Verlag Berlin Heidelberg 2009 2 M. Li et al. data. Adjoint technique is the most widely used one among them(Bennett,1982; Hall,1982; Cacuci,1985; Panchang,1989; Larder,1993; Seiler,1993; Zhu,1997; Vogeler,1999; Zhang,1999; Han,2000,2001; Lu,2002; Heemink,2002; Zhang,2003; Ayoub,2001,2006; Ferron,2003; Gebbie,2004; Ma and Jing,2005). Optimal inversion for parameters, initial and boundary conditions can be obtained by this technique. However, both tidal model equations and adjoint equations need to be calculated.

Input the harmonic constants of interior stations and their corresponding boundary values into data-driven model. After training, the relationships of interior stations and open boundary are generalized. Input the harmonic constants of interior stations analyzed from measurement data into the above relationship and the optimal solution are solved. Step 4: verification of optimal solution Input the optimal solution into the physical numerical model and repeat the computation. The relative error between measurement and results of numerical computation are calculated.

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Advances in Neural Networks – ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part I by Mingchang Li, Guangyu Zhang, Bin Zhou, Shuxiu Liang, Zhaochen Sun (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)


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