Research On Network Model And Application Of Weak Signal Detection Based On Stochastic Resonance Mechanism

Weak signal detection in the background of strong noise is one of the hot and difficult measure technology. It has an important application prospect in the bio-medical, measurement and control, as well as military and other fields. In recent years, stochastic resonance theory and experimental research provided new ideas and methods for weak signal detection.Currently, in the study of weak signal detection based on stochastic resonance, much has been concerned with single or open-loop model. However, in practical applications, these models are vulnerable to the effect of background noise intensity and signal amplitude. Therefore, this essay proposes multi-layer and feedback structure to apply to stochastic resonance model. The response of periodic and non-periodic signal with single, double and feedback structure were studied, stimulated and compared. Take image restoration for example, practical application of weak signal detection based on stochastic resonance is conducted. In this paper, the work and research results are as follows:(1) It is a base of follow-up experiments that the FitzHugh-Nagumo (FHN) neuron model and the bistable model of stochastic resonance phenomenon are studied. The responses of periodic signals and non-periodic signals under the actions are analyzed and the role of noise on stochastic resonance is verified;(2) To simulate the assemble pattern of neurons system, a double-layer FHN neural network is built. Using mutual information rate and other evaluation methods, the stochastic resonance performance of single FHN nerve cell and double-layer FHN neural model is compared by quantitative description in the noise environment detection capability. Experimental results show that the impaction of its detection performance due to the noise intensity and signal amplitude is smaller, compared with a single neuron model, more suitable for dynamic environments weak signal detection;(3) In order to reduce the instability of open-loop network for signal detection in the environment where noise intensity changing frequently, this paper added a feedback link for the double-layer neural network model of FHN. The research result shows that the stochastic resonance phenomenon of the closed-loop neural network model is superior to the double-decker open-loop network and a single neuron. It reflects the law of the input signal within the wider range of noise, and also improves the stability;(4) This article applies stochastic resonance mechanism to low SNR image restoration. Taking full account of the image pixel space correlation, based on the use of 0°and 180°Hilbert scanning method, the dimensionality of two-dimensional image is reduced independently; the polluted image target information is enhanced by adding a specific intensity of the noise with non-linear characteristics of bistable systems; Finally, two one-dimensional sequence of bistable response signals are reconstructed by decision-making. It realized the restoration of the low SNR image. The experimental result indicates that the ability of inhibiting the noise of this method is better, and the reappearance of details is clearer. It still has a better performance in the background of strong noise images (noise intensity = 300) recovery in the subjective evaluation of visual effects and signal to noise ratio, as compared with traditional recovery methods.The research result indicates that signal with noise detection based on stochastic resonance has much better stability in the case of multi-layer and feedback network structure. What's more, the image restoration of the concrete practice shows good prospect of stochastic resonance in weak signal detection area for practical applications.