By Mingchang Li, Guangyu Zhang, Bin Zhou, Shuxiu Liang, Zhaochen Sun (auth.), Wen Yu, Haibo He, Nian Zhang (eds.)
The 3 quantity set LNCS 5551/5552/5553 constitutes the refereed lawsuits of the sixth overseas Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in may well 2009.
The 409 revised papers provided have been conscientiously 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 class, trend acceptance, clever keep an eye on, optimization, robotics, photograph processing, sign processing, biomedical purposes, fault analysis, telecommunication, sensor community and transportation platforms, in addition to applications.
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Extra 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
The tide hydrodynamic is controlled by the tidal waves of the Bohai Sea and Yellow Sea, so the tidal current is complicated. Figure 2 shows the location of computated domain and measurement points. There is only one open boundary line controlled by Qinghuangdao and Changxingdao. Measurement data of tidal elevation from Huludao and tidal current from 69# are used. 1 Choices of Control Variables In the interested ocean bay, the four major tidal constituents can account for more than 90% of total tidal elevation.
Comparison of tidal elevation for Huludao 6 M. Li et al. 6 0 20 40 60 80 100 120 140 Time (Hours) Fig. 4. Comparison of tidal current for 69# Table 1. 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.
In Section 2, we present and compare the ZNN and GNN models/methods for online solution of timevarying matrix square roots. In Section 3, simple but eﬀective software-modeling techniques are investigated for such RNN models. Illustrative veriﬁcation results are presented in Section 4. Finally, we concludes this paper with Section 5. 2 Neural-Network Solvers In the ensuing subsections, the ZNN and GNN models for solving the timevarying matrix square roots problem are developed comparatively. 1 ZNN Model Firstly, to solve time-vary matrix square root A1/2 (t) by Zhang et al’s neuraldynamic method [2,3,4,5,6], we can deﬁne the following matrix-valued error function: E(t) = X 2 (t) − A(t) ∈ Rn×n , where, if the error function E(t) equals zero, X(t) achieves the time-varying theoretical solution A1/2 (t) of the time-varying matrix equation depicted in (1).
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.)