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 complaints of the sixth foreign Symposium on Neural Networks, ISNN 2009, held in Wuhan, China in may well 2009.

The 409 revised papers awarded have been conscientiously 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 platforms, fuzzy platforms and fuzzy neural networks, help vector machines and kernel equipment, genetic algorithms, clustering and class, trend attractiveness, clever regulate, optimization, robotics, photo processing, sign processing, biomedical functions, fault prognosis, telecommunication, sensor community and transportation platforms, in addition to applications.

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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

3) The Constant block generates a constant scalar or constant matrix as specified by its parameter “Constant value”. For example, a 3-dimensional identity matrix is generated by setting “Constant value” to be “[1 0 0; 0 1 0; 0 0 1]”. 4) The Subsystem block is used to construct the sigmoid or power-sigmoid activation-function array, making the whole system simpler and more readable. MATLAB Function du/dt A(t) Derivative Clock ˙ A(t) ˙ X(t)X(t) Matrix Multiply X(t) ˙ X(t) 1 s Integrator ˙ X(t) I − X(t) I − X(t) Matrix Multiply [3x3] Matrix I X 2 (t) − A(t) Matrix Multiply 1 E(t) simout Manual Switch γ To Workspace MATLAB Function norm In1 Out1 powersigmoid Fig.

11–20, 2009. c Springer-Verlag Berlin Heidelberg 2009 12 Y. Zhang, Y. Yang, and N. Tan and methods may not work well, since they could only approximately track the theoretical solution A1/2 (t) with relatively large residual errors [4,5,6]. In contrast, for such time-varying problems, a special kind of RNN models has recently been proposed by Zhang et al [2,3,4,5,6] (formally, since March 2001) for their real-time solution. In this paper, to solve the TVMSR problem, a Zhang neural network (ZNN) model is generalized and designed by using a matrix-valued indefinite error-function, instead of using scalar-valued norm-based lower-bounded energy functions which are usually associated with GNN models and methods.

ZNN model (4) could thus exponentially and superiorly converge to the exact time-varying theoretical solution of (1). In contrast, GNN model (6) has not exploited such important information, thus less effective on solving the timevarying problem. More specifically, when applied to the time-varying problem solving, GNN model (6) could only generate approximate solutions to (1) with much larger steady-state computational errors. , ˙ A(t) in (4)], the ZNN model and method actually belong to a prediction approach, which could be more effective on the system convergence to a “moving” theoretical solution.

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