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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Comparison on Gradient-Based Neural Dynamics and Zhang Neural Dynamics for Online Solution of Nonlinear Equations
verfasst von : Yunong Zhang, Chenfu Yi, Weimu Ma
Erschienen in: Advances in Computation and Intelligence
Verlag: Springer Berlin Heidelberg
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For online solution of nonlinear equation
f
(
x
) = 0, this paper generalizes a special kind of recurrent neural dynamics by using a recent design method proposed by Zhang
et al
. Different from gradient-based dynamics (GD), the resultant Zhang dynamics (ZD) is designed based on the elimination of an indefinite error-monitoring function (instead of the elimination of a square-based positive error-function usually associated with GD). For comparative purposes, the gradient-based dynamics is also developed and exploited for solving online such a nonlinear equation
f
(
x
) = 0. Computer-simulation results via power-sigmoid activation functions substantiate further the theoretical analysis and efficacy of Zhang neural dynamics on nonlinear equations solving.