Adaptive stochastic resonance method based on quantum particle swarm optimization

In order to enhance the usefulness of the theory of stochastic resonance in the areas of weak signal detection, a new method based on quantum particle swarm optimization is proposed to conquer with the problem of adaptive stochastic resonance. First, the problem of adaptive stochastic resonance is converted into the problem of multi-parameter optimization. Then simulation experiments are conducted respectively under a Langevin system and Duffing oscillator system. At the same time, Point detection method is chosen as the comparative test in the Langevin system. While in the Duffing system, the optimization results are compared with those from the Langevin system directly. Results show that the method based on quantum particle swarm optimization is obviously superior to the point detection method and optimization result in the Duffing oscillator is better than that from Langevin system under the same condition. Besides, it is also found that the lower the SNR of input signal, the more effective the quantum particle swarm optimization is. Finally, the regularity of optimization results of the stochastic resonance system parameters is summarized.