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 possibly 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, desktop studying, neural modeling, selection making platforms, fuzzy platforms and fuzzy neural networks, aid vector machines and kernel equipment, genetic algorithms, clustering and class, trend acceptance, clever keep an eye on, optimization, robotics, picture processing, sign processing, biomedical functions, fault prognosis, telecommunication, sensor community and transportation platforms, 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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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
In this paper, two types of f (·) are investigated as examples for the RNN construction: 1)linear activation function f (eij ) = eij ; and, 2)power-sigmoid activation function f (eij ) = epij , 1+exp(−ξ) 1−exp(−ξ) with suitable design parameters ξ · 1−exp(−ξeij ) 1+exp(−ξeij ) , 2 and p 3. if |eij | 1 otherwise (3) Time-Varying Matrix Square Roots Solving via ZNN and GNN 13 Thirdly, expanding ZNN design formula (2) leads to the following implicit dynamic equation of ZNN model for online matrix square roots ﬁnding [in other words, it solves the nonlinear time-varying equation (1)]: ˙ ˙ ˙ X(t)X(t) + X(t)X(t) = −γF X 2 (t) − A(t) + A(t), (4) where X(t), starting from an initial condition X(0) ∈ Rn×n , is the activation state matrix corresponding to theoretical time-varying matrix square root X ∗ (t) := A1/2 (t) of A(t).
The results show the coupled inversion is suitable for realistic open boundary inversion. Compared with adjoint method, present method has two superiorities. One is simplicity. There is no need to deduce and solve complicated adjoint equations. Datadriven model based on ANN is easy to be developed. The other one is its flexibility. In adjoint method, different adjoint equations are needed to be deduced according to different numerical models. , different closure models, the adjoint equations need to be altered accordingly.
If |eij | 1 otherwise (3) Time-Varying Matrix Square Roots Solving via ZNN and GNN 13 Thirdly, expanding ZNN design formula (2) leads to the following implicit dynamic equation of ZNN model for online matrix square roots ﬁnding [in other words, it solves the nonlinear time-varying equation (1)]: ˙ ˙ ˙ X(t)X(t) + X(t)X(t) = −γF X 2 (t) − A(t) + A(t), (4) where X(t), starting from an initial condition X(0) ∈ Rn×n , is the activation state matrix corresponding to theoretical time-varying matrix square root X ∗ (t) := A1/2 (t) of A(t).