具有噪声抑制能力的新型模糊归零神经网络用于求解时变线性方程

IF 10.7 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence Review Pub Date : 2025-02-05 DOI:10.1007/s10462-024-11026-4

Dongsheng Guo, Chan Zhang, Naimeng Cang, Xiyuan Zhang, Lin Xiao, Zhongbo Sun

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引用次数: 0

摘要

近年来，连续/离散型归零神经网络（ZNN）在求解时变线性方程（TVLE）方面取得了成功。本文提出了一种新的模糊归零神经网络（FZNN）模型来解决噪声环境下的TVLE问题。采用积分增强和模糊控制策略，建立了具有抑制噪声能力的FZNN模型。理论分析表明，即使存在噪声，所提出的FZNN模型也能有效地解决TVLE问题。通过不同算例的对比仿真结果进一步验证了所提出的FZNN模型在求解TVLE问题上的有效性和鲁棒性。

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New fuzzy zeroing neural network with noise suppression capability for time-varying linear equation solving

Recently, the zeroing neural network (ZNN) with continuous/discrete-time forms has realized success in solving the time-varying linear equation (TVLE). In this paper, we provide a further investigation by proposing a new fuzzy zeroing neural network (FZNN) model to solve the TVLE in noisy environment. Such a FZNN model, which has the capability of suppressing noise, is developed by using the integration enhancement and fuzzy control strategy. Then, theoretical analysis is presented to show that the proposed FZNN model can effectively solve the TVLE, even with the existence of noise. Comparative simulation results through different examples further verify the effectiveness and robustness of the proposed FZNN model on TVLE solving.

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来源期刊

Artificial Intelligence Review 工程技术-计算机：人工智能

CiteScore

22.00

自引率

3.30%

发文量

194

审稿时长

5.3 months

期刊介绍： Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.