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.
Cite
@article{arxiv.2102.02952,
title = {Robust Adaptive Filtering Based on Exponential Functional Link Network},
author = {T. Yu and W. Li and Y. Yu and R. C. de Lamare},
journal= {arXiv preprint arXiv:2102.02952},
year = {2021}
}