An Efficient Anti-Noise Zeroing Neural Network for Time-Varying Matrix Inverse

Abstract

The Time-Varying Matrix Inversion (TVMI) problem is integral to various fields in science and engineering. Countless studies have highlighted the effectiveness of Zeroing Neural Networks (ZNNs) as a dependable approach for addressing this challenge. To effectively solve the TVMI problem, this paper introduces a novel Efficient Anti-Noise Zeroing Neural Network (EANZNN). This model employs segmented time-varying parameters and double integral terms, where the segmented time-varying parameters can adaptively adjust over time, offering faster convergence speeds compared to fixed parameters. The double integral term enables the model to effectively handle the interference of constant noise, linear noise, and other noises. Using the Lyapunov approach, we theoretically analyze and show the convergence and robustness of the proposed EANZNN model. Experimental findings showcase that in scenarios involving linear, constant noise and noise-free environments, the EANZNN model exhibits superior performance compared to traditional models like the Double Integral-Enhanced ZNN (DIEZNN) and the Parameter-Changing ZNN (PCZNN). It demonstrates faster convergence and better resistance to interference, affirming its efficacy in addressing TVMI problems.