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 may possibly 2009.

The 409 revised papers provided have been rigorously reviewed and chosen from a complete of 1.235 submissions. The papers are geared up in 20 topical sections on theoretical research, balance, time-delay neural networks, computer studying, neural modeling, determination making platforms, fuzzy structures and fuzzy neural networks, aid vector machines and kernel tools, genetic algorithms, clustering and type, trend reputation, clever regulate, optimization, robotics, snapshot processing, sign processing, biomedical functions, fault prognosis, telecommunication, sensor community and transportation structures, in addition to applications.

**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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**Extra 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**

In present paper, the results of ANN are used for the fitting of F . 2 Back-Propagation Neural Network The BPNN proposed by Rumelhart et al. (1986) is the most commonly used among the entire artificial neural network models. The BPN uses the gradient steepest descent method to determine the weight of connective neurons. The key point is the error back-propagation technique. In the learning process of the BPN, the Optimal Inversion of Open Boundary Conditions Using BPNN Data-Driven Model 3 interconnection weights are adjusted from back layers to front layers to minimize the output error.

Overall ZNN Simulink model which solves for time-varying matrix square roots 16 Y. Zhang, Y. Yang, and N. Tan u X(t) X T (t) T transpose ˙ X(t) γX T (t)F X 2 (t) − A(t) 1 s Integrator γF X 2 (t) − A(t) X T (t) MATLAB Function Clock Matrix Multiply Matrix Multiply A(t) X 2 (t) − A(t) Matrix Multiply 1 γ E(t) simout To Workspace MATLAB Function norm Fig. 2. Overall GNN Simulink model applied to time-varying square roots solving 5) The MATLAB Fcn block can be used to generate matrix A(t) with the Clock block’s output as its input or can be used to compute the matrix norm.

4 Table 2. Values for phase (º)of control nodes along open boundary Tidal constituents M2 K1 Qinghuang dao -140 -155 -170 120 135 150 Changxing dao 50 65 80 30 45 60 Tidal constituents S2 O1 Qinghuang dao -60 -75 -89 10 25 40 Changxing dao 100 115 130 55 70 85 An interesting phenomenon is founded by analyzing the result of designed cases, that is the tidal amplitude is not affected by the change of tidal phase in its assumed range. Therefore, the amplitude and phase for each control variables are inversed independently.

### 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.)

by Richard

4.4