Title :
A new neural network approach of linear programming
Author_Institution :
Sch. of Sci., Hangzhou Dianzi Univ., Hangzhou
Abstract :
This paper presents a new neural network for solving linear programming problems. After defining the neural dynamics of the proposed neural network, we have shown the existence of an equilibrium point and the asymptotic stability of the equilibrium point of the neural dynamics. As the time parameter approaches infinity, an optimal solution of the linear programming problem is shown to be the equilibrium point of the neural dynamics, and each equilibrium point is optimal to the problem. Two numerical results indicate that the proposed neural network is an efficient technique and stable independent of the starting point.
Keywords :
asymptotic stability; linear programming; neural nets; equilibrium point asymptotic stability; linear programming problems; neural dynamics; neural network; Artificial neural networks; Asymptotic stability; Differential equations; Dynamic programming; Electronic mail; H infinity control; Linear programming; Machine learning; Neural networks; Operations research; Asymptotically stable; Differential equation; Linear programming; Neural network;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620499