Nonlinear system identification using exact and approximate improved adaptive exponential functional link networks

Abstract

Adaptive exponential functional link network (AEFLN) is a recently introduced linear-in-the-parameters nonlinear filter. In an attempt to improve the performance of AEFLN, an improved AEFLN (IAEFLN) which employs independent decay rates for each exponentially varying sinusoidal basis function, has been proposed in this brief. The update rules for the filter weights as well as the decay parameter vector has been derived. To further reduce the computational complexity of the proposed network, without sacrificing performance, two approximate versions of IAEFLN, namely approximate 1 IAEFLN (Apx1-IAEFLN) and approximate 2 IAEFLN (Apx2-IAEFLN) has been developed and their corresponding update rules have been derived. Bounds on learning rates have also been derived and simulation study shows improved convergence behaviour of the proposed IAEFLN over AEFLN. The approximate versions achieve similar convergence performance as that of IAEFLN, at a lower computational load.