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A Lower Order Recurrent Neural Network for Solving Higher Order Quadratic Programming Problems with Equality Constraints

By selecting an appropriate transformation of the ariables in quadratic programming problems with equality constraints, a lower order recurrent neural network for solving higher quadratic programming is presented. The proposed recurrent neural network is globally exponential stability and converges to the optimal solutions of the higher quadratic programming. An op-amp based on the analogue circuit realization of the recurrent neural network is described. The recurrent neural network proposed in the paper is simple in structure, and is more stable and more accuracy for solving the higher quadratic programming than some existed conclusions, especially for the case that the number of decision variables is close to the number of the constraints. An illustrative example is discussed to show us how to design the analogue neural network using the steps proposed in this paper.