Blind Source Separation Method Based on Genetic Variation Optimization for Second-Order Oscillation Particle Swarm

Abstract

The invention provides a two-order oscillation particle swarm blind source separation method based on heritable variation optimization and belongs to the technical field of blind signal processing. The method solves the problem that in a traditional blind source separation algorithm, a nonlinear activation function is difficult to select, and mixed signals can be effectively separated under the premise that source signal priori knowledge is not available. Separation signal negative entropy serves as a target function, and the local searching capability and the global searching capability are balanced with fixed inertia weights; population diversity can be maintained under the condition that the number of particles is unchanged by introducing the study factor two-order oscillation link; a heritable variable mechanism is introduced, and thus the situation of convergence rate reduction caused by introduction of two-order oscillation can be easily relieved. It is shown through separation of simulation vibration signals and chaotic mapping signals that the method can be applied to the mechanical signal fault detection field and aspects such as determined noise-like signal processing. By means of the method, the improved type theoretical study of intelligent algorithm blind source separation is supplemented, and great significance is achieved on separation of unknown mixed signals in engineering application.

Description

technical field [0001] The invention belongs to the field of blind separation and processing of unknown mixed signals, specifically the problem of separating mixed signals when the prior knowledge of the source signal is unknown by adding a second-order oscillation link on the basis of the basic particle algorithm and introducing genetic variation for optimization. Background technique [0002] The blind source separation algorithm studies statistically independent non-Gaussian signals, and separates mixed signals when the source signal and the prior knowledge of the transmission channel are unknown, which has greatly promoted the field of blind signal processing. Separating mixed signals in stationary, non-stationary and noisy environments is an important research topic in the application of blind source separation theory. Traditional blind source separation algorithms mostly deal with the separation of mixed observation signals in stationary environments. Since the transmi...

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Problems solved by technology

[0003] The technical problem to be solved by the present invention is that the implementation of the classic blind source separation algorithm will involve the problem of selecting a nonlinear function according to the source signal probability density properties and kurtosis values ​​for separation operations, which is different from the unknown source signal and channel properties. contradiction

At the same time, it is difficult for these algorithms to jump out of the local optimum during the separation operation, and the convergence speed is slow, which affects the separation effect. Therefore, in recent years, research tends to apply intelligent algorithms to blind source separation.

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