Neural Networks Applied in Linear Programming Problems: Design and Complexity Analysis

Artificial neural networks are richly connected networks of simple computational elements modeled on biological processes. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving linear programming problems. The internal parameters of the network are obtained using the valid-subspace technique. A complexity analysis between the main neural networks used in linear programming is also developed. Simulated examples are presented as an illustration of the proposed approach. Key-Words: Artificial neural networks, linear programming, complexity analysis, operations research, artificial intelligence, systems optimization.