Self-adaption stochastic resonance weak signal detecting method based on particle swarm optimization algorithm

A technology for weak signal detection and particle swarm optimization, applied in computing, measuring devices, computing models, etc., can solve problems such as difficult adaptive selection and limited application

Inactive Publication Date: 2012-10-17
TIANJIN UNIV
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Problems solved by technology

[0004] Aiming at weak signal detection under strong noise background and large parameter conditions, variable step size stochastic resonance can break through the limitation of adiabatic approximation theory on small parameters, so it can be applied to engineering ac

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  • Self-adaption stochastic resonance weak signal detecting method based on particle swarm optimization algorithm
  • Self-adaption stochastic resonance weak signal detecting method based on particle swarm optimization algorithm
  • Self-adaption stochastic resonance weak signal detecting method based on particle swarm optimization algorithm

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[0050] The method for detecting a weak signal of adaptive stochastic resonance based on the particle swarm optimization algorithm of the present invention will be described in detail below in combination with the embodiments and the accompanying drawings.

[0051] figure 1 Shown is the basic flow chart of the present invention, the present invention is described in further detail below in conjunction with accompanying drawing: the adaptive stochastic resonance weak signal detection method based on particle swarm optimization algorithm of the present invention, comprises the following stages:

[0052] 1) Initialization of particle population

[0053] To apply particle swarm optimization algorithm to stochastic resonance with variable step size, it is first necessary to initialize a group of particle populations.

[0054] Particle Swarm Optimization (PSO) was proposed by Kennedy and Eberhart in 1995 after studying the foraging behavior of birds. The algorithm is easy to implem...

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Abstract

The invention relates to a self-adaption stochastic resonance weak signal detecting method based on a particle swarm optimization algorithm. The method comprises the following steps of 1) particle swarm initialization; 2) step-changed stochastic resonance; 3) individual fitness evaluation; 4) particle speed and position updating; 5) termination condition judgment and 6) detection result output. The self-adaption stochastic resonance weak signal detecting method has the advantages that the simplicity is realized, the implementation is easy, the application range is wide, the convergence speed is high, high-frequency weak signals at high-noise background can be effectively detected, and a novel method is provided for stochastic resonance parameter self-adaption selection and practical application in engineering.

Description

technical field [0001] The invention relates to a weak signal detection method. In particular, it involves an adaptive stochastic resonance based on particle swarm optimization algorithm that can adaptively select the optimal variable step size stochastic resonance system structural parameters and calculation step size, and can effectively detect weak signals under large parameter conditions. Weak signal detection method. Background technique [0002] Since Benzi et al. proposed the concept of stochastic resonance (SR) in 1981 in the study of paleoclimate glaciers, the SR phenomenon has received extensive attention. The stochastic resonance phenomenon is a nonlinear phenomenon. Under certain conditions, it transfers part of the noise energy to the signal. While reducing the noise, it can resonate and strengthen the weak signal submerged in the noise, greatly improving the output signal-to-noise Ratio, so as to achieve the purpose of detecting weak signals from strong noise...

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Application Information

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IPC IPC(8): G01H17/00G06N3/00
Inventor 王太勇张仲海林锦州王多耿博
Owner TIANJIN UNIV
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