Abstract
In this paper we present a neural network model for solving the nonlinear complementarity problem. This model is derived from an equivalent unconstrained minimization reformulation of the complementarity problem, which is based on a one-parametric class of nonlinear complementarity func- tions. We establish the existence and convergence of the trajectory of the neural network, and we study its Lyapunov stability, asymptoti stabilityc as well as exponential stability. Numerical tests verify the obtained theoretical results. Keywords: Neural network, nonlinear complementarity problem, stability, reformulation To cite this article: F. Arenas, R. Perez, H. Vivas, Un modelo de redes neuronales para complementariedad no lineal, Rev. Integr. Temas Mat. 34 (2016), No. 2, 169-185.
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