扰动梯度神经网络求解LMS问题的收敛性和鲁棒性分析

Wudai Liao, Xingfeng Wang, Yuyu Yang, Junyan Wang
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摘要

本文介绍了一种基于梯度神经网络的求解最小均方问题的方法,包括网络模型的构建、网络全局收敛性的定量分析以及不同激活函数下网络的收敛速度。MATLAB仿真结果与理论分析结果相吻合,进一步证实了基于Hopfield神经网络的方法在求解最小均方问题上具有良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convergence and robustness analysis of disturbed gradient neural network for solving LMS problem
In this paper, we introduce a kind of method for solving least mean square problems based on the gradient neural network, including the network model construction, quantitative analysis of the network global convergence and the network convergence rate about the different activation functions. MATLAB simulation results and theoretical analysis results are accordingly consistent, which further confirm the method based on Hopfield neural network has a good effect on solving the least mean square problems.
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