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Noisy Independent Component Analysis Method Based on Invasive Weed Algorithm

An independent component analysis and algorithm technology, applied in the field of signal processing, can solve problems such as poor separation effect and falling into local optimum, and achieve the effect of solving poor separation effect

Inactive Publication Date: 2017-03-01
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the traditional ICA algorithm has poor separation effect in the case of noise and is easy to fall into local optimum, and invented a noisy independent component analysis method based on the invasive weed algorithm

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  • Noisy Independent Component Analysis Method Based on Invasive Weed Algorithm
  • Noisy Independent Component Analysis Method Based on Invasive Weed Algorithm
  • Noisy Independent Component Analysis Method Based on Invasive Weed Algorithm

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Embodiment Construction

[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0032] The present invention selects the Gaussian density function to estimate the fitness function of the invasive weed (IWO) algorithm, and adopts the invasive weed algorithm with global optimization performance to estimate the separation matrix, and its specific flow chart is as follows figure 1 shown. Construct the following three source signals s1=sin(2*pi*0.003*t), s2=sin(2*pi*0.01*t).*sin(2*pi*0.0007*t), s3=((rem( t / 20,22)-11) / 9).^5.

[0033] Combining the above three signals in order into a source signal s, the source signal s is as follows figure 2 Shown, and multiply s with the randomly generated mixing matrix A, the mixing matrix A is

[0034] Gaussian noise n is added after mixing the source signal s to obtain a noisy mixed signal x with a signal-to-noise ratio of 10dB, that is, x=As+n, such as image 3 As shown, us...

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Abstract

The invention belongs to the technical field of signal processing, and discloses a noisy independent component analysis method based on an invasive weed algorithm. The method uses an invasive weed algorithm to estimate a separation matrix, and its specific content includes the following steps: 1. Centralization and robust whitening processing; 2 Apply the invasive weed algorithm to optimize the signal after whitening processing to obtain the best separation matrix Wb; 3 According to the noise-containing separation signal y; 4 According to the noise-containing separation signal y, use a single-channel Underdetermined SVD-ICA algorithm, seeking noise-free separation signal The beneficial effect of the present invention is: the separation matrix is ​​optimized by using the invasive weed algorithm, and the global best separation matrix can be obtained, which solves the problem of the traditional independent component analysis method in noise-containing In this case, the separation effect is not good, and it is easy to fall into the problem of local extremum. The simulation results show that, compared with the traditional independent component analysis method, this method can estimate the mixing matrix more accurately, and the similarity between the separated signal and the source signal is higher.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to a noisy independent component analysis method based on an invasive weed algorithm. Background technique [0002] Independent component analysis (ICA for short) refers to a statistical method for extracting signal sources when there are only observed data and the aliasing method of signal sources is unknown. As an effective blind source separation technique, ICA is a hot spot in the field of signal processing. In recent years, ICA has been widely used in wireless communication, biomedicine, image speech, flow pattern recognition, fault diagnosis and other fields, and has significant theoretical significance and practical value. (References: [1] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis [J]. Neural Networks, IEEE Transactions on, 1999, 10(3): 626-634.) [0003] Invasive Weed Optimization (IWO for short) algorithm ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/00
Inventor 王微微孔祥翠陈静静梁霄成帅帅
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)