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Chen, J.-S., Ko, C.-H. and Pan, S.H. (2010) A Neural Network Based on the Generalized Fischer-Burmeister Function for Nonlinear Complementarity Problems. Information Sciences, 180, 697-711.
http://dx.doi.org/10.1016/j.ins.2009.11.014

has been cited by the following article:

• TITLE: A Smoothing Neural Network Algorithm for Absolute Value Equations

  AUTHORS: Feiran Wang, Zhensheng Yu, Chang Gao

  KEYWORDS: Absolute Value Equations, Neural Network, Smoothing Function, Linear Complementarity Problem

  JOURNAL NAME: Engineering, Vol.7 No.9, September 21, 2015

  ABSTRACT: In this paper, we give a smoothing neural network algorithm for absolute value equations (AVE). By using smoothing function, we reformulate the AVE as a differentiable unconstrained optimization and we establish a steep descent method to solve it. We prove the stability and the equilibrium state of the neural network to be a solution of the AVE. The numerical tests show the efficient of the proposed algorithm.