Jixie qiangdu (Jan 2017)
RESEARCH ON ARRAY-ENHANCED SELF-ADAPTIVE STOCHASTIC RESONANCE BASED ON PARTICLE SWARM OPTIMIZATION
Abstract
Aiming at the disadvantages of traditional method of stochastic resonance（SR）,for example,the SNR of output response is low and the self-adaptive time of parameters is long for single self-adaptive SR,and the parameters are hard to set for array-enhanced SR,a array-enhanced self-adaptive SR based on extremum disturbed and simple particle swarm optimization（tsPSO） algorithm was proposed,which has realized an effective and fast detection of the weak signals under conditions of large parameters under high strong noise background. Firstly,the parallel SR system was adopted to enhance the SNR of the final output response by analyzing the output responses of sub-systems with the theory of auto correlation analysis and composing them.Secondly,in each sub-system in parallel,the cascaded SR system was adopted to enhance the SNR of output responses further.Finally,the parameters in each sub-systems in parallel were optimized with the SNR of output response as the fitness function,and the sectioning search and tsPSO algorithm were used to shorten the self-adaptive time of the parameters at the same time. The effectiveness of the method in the paper were proved by the results of simulation experiment and engineering application.
