Abstract
Recurrent artificial neural network (ANN) models are presented for solving primal-dual linear programming problems. Theoretical background is introduced based on the nonlinear analysis of ANN. A method to reduce the circuit complexity of the proposed ANN from the order of O(mn) to 0(m+n) is developed. Simulation results are demonstrated through an example of up to 20 variables.