An adaptive stochastic resonance detection method based on a fast artificial fish swarm algorithm

Abstract

A new stochastic resonance method that is based on a fast artificial fish swarm algorithm has been proposed in an effort to address the adaptive parameter-induced stochastic resonance for weak signal detection’s slow convergence time. The target evaluation function for the system is the output signal-to-noise ratio. The method of scale transformation and amplitude compression is used to pre-process the high frequency and large parameter signals. To achieve fast adaptive detection that applies to weak communication signals, the stochastic resonance system’s characteristics are used to constrain the optimization iteration rules.According to the simulation results, the fast artificial fish swarm method has significantly better optimization efficiency and achieves the same optimization results as the basic artificial fish swarm algorithm while reducing convergence time by 74.89%.