Kobra Mohammadsalahi; Farzin Modarres Khiyabani; Nima Azarmir Shotorbani
Abstract
This paper presents a capable recurrent neural network, the so-called µRNN for solving a class of non-convex quadratic programming problems. Based on the optimality conditions we construct a new recurrent neural network (µRNN), which has a simple structure and its capability ...
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This paper presents a capable recurrent neural network, the so-called µRNN for solving a class of non-convex quadratic programming problems. Based on the optimality conditions we construct a new recurrent neural network (µRNN), which has a simple structure and its capability is preserved. The proposed neural network model is stable in the sense of Lyapunov and converges to the exact optimal solution of the original problem. In a particular case, the optimality conditions of the problem become necessary and sufficient. Numerical experiments and comparisons with some existing algorithms are presented to illustrate the theoretical results and show the efficiency of the proposed network.
