A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems
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Abstract:
In this paper we present an improved neural network to solve strictly convex quadratic programming(QP) problem. The proposed model is derived based on a piecewise equation correspond to optimality condition of convex (QP) problem and has a lower structure complexity respect to the other existing neural network model for solving such problems. In theoretical aspect, stability and global convergence of the proposed neural network is proved.
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Journal title
volume 10 issue 4
pages 339- 347
publication date 2018-11-01
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