Robust Adaptive Filtering Based on Exponential Functional Link Network

TL;DR

This paper introduces a robust adaptive filtering algorithm based on exponential functional link networks and a novel inverse square root cost function, demonstrating improved performance under impulsive interference in nonlinear system identification.

Contribution

It proposes the EFLN-ISR algorithm, a new adaptive filtering method that enhances robustness against impulsive noise, with theoretical analysis and practical validation.

Findings

Robust steady-state performance under impulsive interference

Effective nonlinear system identification demonstrated

Theoretical derivation confirmed by numerical simulations

Abstract

The exponential functional link network (EFLN) has been recently investigated and applied to nonlinear filtering. This brief proposes an adaptive EFLN filtering algorithm based on a novel inverse square root (ISR) cost function, called the EFLN-ISR algorithm, whose learning capability is robust under impulsive interference. The steady-state performance of EFLN-ISR is rigorously derived and then confirmed by numerical simulations. Moreover, the validity of the proposed EFLN-ISR algorithm is justified by the actually experimental results with the application to hysteretic nonlinear system identification.