# Robust Adaptive Filtering Based on Exponential Functional Link Network

arXiv: https://arxiv.org/abs/2102.02952 · HTML: https://mlsanity.com/paper/2102.02952/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 44 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques](https://mlsanity.com/paper/1606.01313/) (2016)
- [Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage](https://mlsanity.com/paper/1401.0463/) (2014)
- [Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents](https://mlsanity.com/paper/1812.09985/) (2018)

## Built on by (most cited first, 3 of 3)

- [Study of Frequency domain exponential functional link network filters](https://mlsanity.com/paper/2201.05501/) (2022)
- [Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate](https://mlsanity.com/paper/2203.10205/) (2022)
- [Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks](https://mlsanity.com/paper/2206.12749/) (2022)

## Abstract (arXiv)

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.