A neural network based on the generalized Fischer-Burmeister function for nonlinear complementarity problems


In this paper, we consider a neural network model for solving the nonlinear complementarity problem (NCP). The neural network is derived from an equivalent unconstrained minimization reformulation of the NCP, which is based on the generalized Fischer–Burmeister function /pða; bÞ 1⁄4 kða; bÞkp ðaþ bÞ. We establish the existence and the convergence of the… (More)
DOI: 10.1016/j.ins.2009.11.014


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