A Recurrent Neural Network for Solving Complex-Valued Quadratic Programming Problems with Equality Constraints

摘要

A recurrent neural network is presented for solving systems of quadratic programming problems with equality constraints involving complex-valued coefficients. The proposed recurrent neural network is asymptotically stable and able to generate optimal solutions to quadratic programs with equality constraints. An opamp based analogue circuit realization of the recurrent neural network is described. An illustrative example is also discussed to demonstrate the performance and characteristics of the analogue neural network.