Design and Validation of Zeroing Neural Network With Active Noise Rejection Capability for Time-Varying Problems Solving

Abstract

Recently, the zeroing neural network (ZNN) has demonstrated remarkable effectiveness in tackling time-varying problems, delivering robust performance across both noise-free and noisy environments. However, existing ZNN models are limited in their ability to actively suppress noise, which constrains their robustness and precision in solving time-varying problems. This paper introduces a novel active noise rejection ZNN (ANR-ZNN) design that enhances noise suppression by integrating computational error dynamics and harmonic behaviour. Through rigorous theoretical analysis, we demonstrate that the pro-posed ANR-ZNN maintains robust convergence in computational error performance under environmental noise. As a case study, the ANR-ZNN model is specifically applied to time-varying matrix inversion. Comprehensive computer simulations and robotic experiments further validate the ANR-ZNN's effectiveness, emphasising the proposed design's superiority and potential for solving time-varying problems.

Keywords

active noise rejection / matrix inversion / robot application / time-varying problems / zeroing neural network

Funding

This research was supported by the National Science and Technology Major Project (2022ZD0119901), the National Natural Science Foundation of China under Grant (U2141234, 62463004 and U24A20260), the Hainan Province Science and Technology Special Fund (ZDYF2024GXJS003) and the Scientific Research Fund of Hainan University (KYQD(ZR)23025).

Conflicts of Interest

The authors declare no confiicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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