Recurrent neural network for solving quadratic-programming problems with equality constraints

A recurrent neural network for solving quadratic programming problems with equality constraints is presented. 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.