Download Advances in Neural Networks – ISNN 2009: 6th International by Mingchang Li, Guangyu Zhang, Bin Zhou, Shuxiu Liang, PDF

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 court cases of the sixth foreign Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in might 2009.

The 409 revised papers offered have been conscientiously reviewed and chosen from a complete of 1.235 submissions. The papers are prepared in 20 topical sections on theoretical research, balance, time-delay neural networks, desktop studying, neural modeling, choice making structures, fuzzy platforms and fuzzy neural networks, help vector machines and kernel tools, genetic algorithms, clustering and category, trend acceptance, clever keep an eye on, optimization, robotics, photo processing, sign processing, biomedical functions, fault analysis, telecommunication, sensor community and transportation platforms, in addition to applications.

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

Sample text

The results are shown in table 4. 76° from O1, both in 69#. The error indicates coupled inversion can inverse realistic open boundary values in better accuracy. Table 4. 007 8 M. Li et al. Table 5. 76 5 Conclusion In this paper, a new method is developed to inverse open boundary values. In the method, data–driven model and physical tidal model are coupled automatically. Physical numerical model repeat a number of computation for designed cases, the results of tidal elevation are stored and output for data-driven model.

Optimal boundary conditions are estimated by data-driven model which is based on artificial neural network. POM (Blumberg and Mellor 1987) is employed to simulate the tidal hydrodynamics for interested area. The structure of the paper is as follows. In section 2, the basic idea and theory of data-driven, Back-Propagation Neural Network (BPNN) and tidal numerical model are introduced briefly. The detailed steps about how to inverse open boundary are described in section 3. In section 4, the method in section 3 is verified with Liaodong bay.

The Importance of Open-Boundary Estimation for an Indian Ocean GCM-data Synthesis. Journal of Marine Research 57, 305–334 (1999) 14. : Optimizing Open Boundary Conditions of Nonlinear Tidal Model Using Adjoint Method: The Establishment of Adjoint Model and Twin-Experiment. ACTA Oceanologica SINICA 22(6), 27–33 (2000) 15. : Optimizing Open Boundary Conditions of Nonlinear Tidal Model Using Adjoint Method. Assimilation Experiment for Tide in the Huanghai Sea and the East China Sea. ACTA Oceanologica SINICA 23(2), 25–31 (2001) 16.

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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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