A discrete-time recurrent neural network for solving quadratic programs with application to FIR filter synthesis

A discrete-time recurrent neural network is presented for solving convex quadratic programs. It is the discrete-time version of its continuous-time counterpart which was developed by J. Wang and H. Li (1994). Sharing the same characteristic with its continuous-time counterpart, the proposed discrete-time neural network could compute the exact optimal solution to a quadratic program without using any penalty parameter. However, the discrete-time version is more desirable in practical realization in view of the availability of digital hardware and the good compatibility to computer. The condition for the neural network globally converging to the optimal solution of a quadratic program is given. The neural network is applied to FIR filter synthesis for illustrating its effectiveness