论文信息 - Augmented Hopfield network for mixed-integer programming

Augmented Hopfield network for mixed-integer programming

Watta and Hassoun recently proposed a coupled gradient neural network for mixed integer programming. In this network continuous neurons were used to represent discrete variables. For the larger temporal problem they attempted many of the solutions found were infeasible. This letter proposes an augmented Hopfield network which is similar to the coupled gradient network proposed by Watta and Hassoun. However, in this network truly discrete neurons are used. It is shown that this network can be applied to mixed integer programming. Results illustrate that feasible solutions are now obtained for the larger temporal problem.

Michael P. Walsh | Mark J. O'Malley | Meadhbh E. Flynn

[1] Hiroshi Sasaki,et al. A solution method of unit commitment by artificial neural networks , 1992 .

[2] Mohamad H. Hassoun,et al. A coupled gradient network approach for static and temporal mixed-integer optimization , 1996, IEEE Trans. Neural Networks.

[3] Mark O'Malley,et al. Augmented Hopfield network for unit commitment and economic dispatch , 1997 .

[4] Kwang Y. Lee,et al. Economic load dispatch for piecewise quadratic cost function using Hopfield neural network , 1993 .

[5] Ferial El-Hawary,et al. Optimal environmental dispatching of electric power systems via an improved Hopfield neural network model , 1995 .

[6] John J. Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities , 1999 .

[7] F. E. Villaseca,et al. Neural networks for generation scheduling in power systems , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[8] Michael Peter Kennedy,et al. Unifying the Tank and Hopfield linear programming circuit and the canonical nonlinear programming circuit of Chua and Lin , 1987 .