A Recurrent Neural Network for Solving Strictly Convex Quadratic Programming Problems
محورهای موضوعی : مجله بین المللی ریاضیات صنعتی
1 - Department of Mathematics, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
2 - Department of Mathematics, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
کلید واژه: Recurrent neural network, Global convergence, Dynamical system, Stability, Strictly convex quadratic programming,
چکیده مقاله :
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.
