Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11124
Title: Solving nonlinear complementarity problems with neural networks : a reformulation method approach
Authors: Liao, LZ
Qi, H
Qi, L 
Keywords: Neural network
Nonlinear complementarity problem
Stability
Reformulation
Issue Date: 2001
Publisher: North-Holland
Source: Journal of computational and applied mathematics, 2001, v. 131, no. 1-2, p. 343-359 How to cite?
Journal: Journal of computational and applied mathematics 
Abstract: In this paper, we present a neural network approach for solving nonlinear complementarity problems. The neural network model is derived from an unconstrained minimization reformulation of the complementarity problem. The existence and the convergence of the trajectory of the neural network are addressed in detail. In addition, we also explore the stability properties, such as the stability in the sense of Lyapunov, the asymptotic stability and the exponential stability, for the neural network model. The theory developed here is also valid for neural network models derived from a number of reformulation methods for nonlinear complementarity problems. Simulation results are also reported.
URI: http://hdl.handle.net/10397/11124
ISSN: 0377-0427
EISSN: 1879-1778
DOI: 10.1016/S0377-0427(00)00262-4
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