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Improved wavelet threshold function denoising method based on particle swarm algorithm

A particle swarm algorithm and wavelet threshold technology, applied in the direction of calculation, calculation model, computer components, etc., can solve the problems of poor noise reduction ability of wavelet threshold and poor adaptability of traditional threshold function, and achieve the effect of improving noise reduction ability.

Pending Publication Date: 2021-01-01
SHENYANG POLYTECHNIC UNIV
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Problems solved by technology

[0005] The present invention proposes an improved wavelet threshold function denoising method based on the particle swarm algorithm, and its purpose is to solve the problems of poor adaptability of the traditional threshold function and poor denoising ability of the wavelet threshold

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  • Improved wavelet threshold function denoising method based on particle swarm algorithm
  • Improved wavelet threshold function denoising method based on particle swarm algorithm
  • Improved wavelet threshold function denoising method based on particle swarm algorithm

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Embodiment Construction

[0044] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0045] Such as figure 1 , the described a kind of improved wavelet threshold function denoising method based on particle swarm optimization, comprising the following steps:

[0046] Step 1. Obtain the noisy signal, select the wavelet basis function and the number of decomposition layers to decompose the noisy signal, and obtain the original wavelet coefficient x;

[0047] In this embodiment, first obtain MATLAB's own such as figure 2 The original wcleandata signal with 1000 sampling points is shown, and the N(0,σ 2 ) distributed Gaussian white noise, get image 3 For the noisy signal shown, the original wavelet coefficient x is obtained by using the sym4 wavelet basis function to decompose the noisy signal at four levels.

[0048] With the increase of denoising times and decomposition layers, the noise energy in the wavelet coefficients be...

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Abstract

The invention relates to an improved wavelet threshold function denoising method based on a particle swarm algorithm, and the method comprises the following steps: obtaining a noisy signal, and obtaining an original wavelet coefficient; substituting the original wavelet coefficient into an improved wavelet threshold function containing a to-be-optimized threshold parameter; determining an optimalvalue of the improved wavelet threshold function threshold parameter in the step 2 by using a particle swarm algorithm; substituting the optimal value of the threshold parameter into an improved wavelet threshold function, and performing threshold processing on the wavelet coefficient by adopting a unified threshold method to obtain a wavelet coefficient after threshold processing; and reconstructing the wavelet coefficient after threshold processing to obtain a denoised signal. The wavelet threshold denoising method has the adaptability to the preprocessed signals, the wavelet threshold denoising capacity is improved, and real information of the original signals is reserved.

Description

technical field [0001] The invention belongs to the technical field of wavelet signal denoising, in particular to an improved wavelet threshold function denoising method based on particle swarm algorithm. Background technique [0002] In practical engineering applications, signal collection and processing are often interfered by varying degrees of noise, reducing the effectiveness of the signal, and even causing the signal to fail. Therefore, in order to remove the superimposed noise or interference components in the original signal, a wavelet noise reduction method with better time-frequency and multi-resolution characteristics has emerged. Among the many denoising methods, the most commonly used is the wavelet threshold denoising algorithm which is affected by many factors such as wavelet base type, decomposition layer number, threshold estimation criterion, and threshold function form, among which the most important one is the threshold function form. The traditional thr...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/00
CPCG06N3/006G06F2218/06
Inventor 徐方素孙兴伟董祉序杨赫然刘慧芳孙凤刘伟军刘寅
Owner SHENYANG POLYTECHNIC UNIV
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