matlab基于梯度下降和软阈值化的去噪算法

1、内容简介

matlab136-基于梯度下降和软阈值化的去噪算法

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2、内容说明

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Diagnosing faults in large mechanical equipment poses challenges due to strong background

noise interference, wherein extracting weak fault features with periodic group-sparse property is

the most critical step for machinery intelligent maintenance. To address this problem, a periodic

group-sparse method based on a generalized minimax-concave penalty function is proposed in

this paper. This method uses periodic group sparse techniques to capture the periodic clustering

trends of fault impact signals. To further enhance the sparsity of the results and preserve the

high amplitude of the impact signals, non-convex optimization techniques are integrated. The

overall convexity of the optimization problem is maintained through the introduction of a

non-convex controllable parameter, and an appropriate optimization algorithm is derived. The

effectiveness of this method has been demonstrated through experiments with simulated signals

and mechanical fault signals.

Keywords: non-convex penal

3、仿真分析

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4、参考论文

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