Robust Data-Selective Nonlinear System Identification Based on Volterra Model

The article delves into the critical challenges faced in nonlinear system identification, particularly the lack of effective mathematical tools and the presence of non-Gaussian noise. It introduces a data-selective Volterra filtering algorithm based on the Variable Maximum Correntropy Criterion (VMCC), which offers robust resistance to impulse noise and significantly reduces computational costs. The proposed algorithm dynamically adjusts data selection thresholds, ensuring optimal performance across diverse noise environments. Through detailed simulations and performance analyses, the article demonstrates the superior accuracy and efficiency of the proposed method compared to traditional approaches. The findings highlight the algorithm's potential applications in various fields, such as wireless communication and acoustic feedback cancellation, where noise resistance and computational efficiency are paramount. The article concludes with a discussion on the algorithm's theoretical innovations and practical implications, paving the way for future advancements in nonlinear system identification.