Noisy independent component analysis method based on invasiveness 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: 2014-03-26
CHINA UNIV OF PETROLEUM (EAST CHINA)
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[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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[0032] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0033] 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.
[0034] 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 A = - 0.4326 0.2877 1.1892 ...
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The invention belongs to the technical field of signal processing, and discloses a noisy independent component analysis method based on an invasiveness weed algorithm. According to the noisy independent component analysis method, the invasiveness weed algorithm is adopted for estimating separation matrixes; the noisy independent component analysis method particularly includes the following steps: 1 conducting centralization and robust whitening processing on observed signals; 2 optimizing the whitened signals with the invasiveness weed algorithm to obtain the best separation matrix Wb; 3 obtaining noisy separation signals y; 4 obtaining noise-free separation signals through a one-way underdetermined SVD-ICA algorithm according to the noisy separation signals y. The noisy independent component analysis method has the advantages that the invasiveness weed algorithm is adopted for optimizing the separation matrixes, the overall best separation matrix can be obtained, and the problem that a traditional independent component analysis method is poor in separating effect and prone to falling into a local extremum under the noisy condition is solved. Simulation results indicate that compared with the traditional independent component analysis method, the noisy independent component analysis method can accurately estimate a hybrid matrix, and the similarity between separated signals and source signals is high.
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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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)