A new regression estimator with neural network realization

Abstract

A new regression estimator viewed as the solution of a strictly convex quadratic programming problem is introduced in this paper. Two recurrent neural networks in continuous-time and discrete-time respectively are proposed to solve the quadratic programming problem in real time. The continuous-time neural network is shown to have a global stability, including the global asymptotic and exponential stability. The discrete-time neural network is shown to have a global convergence with a fixed step length. This fixed step length can be independent of the regression problem size by scaling a design parameter. Since the sizes of the proposed neural networks depend only on the constraints of the optimization problems, the proposed new regression estimator can obtained by two novel neural networks with lower implementation costs than the conventional methods. Our simulation results confirm that the proposed neural networks are effective in solving various kind of regression problems.