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Adaptive stochastic resonance method based on quantum particle swarm optimization

Li Yi-Bo Zhang Bo-Lin Liu Zi-Xin Zhang Zhen-Yu

Adaptive stochastic resonance method based on quantum particle swarm optimization

Li Yi-Bo, Zhang Bo-Lin, Liu Zi-Xin, Zhang Zhen-Yu
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  • Received Date:  20 March 2014
  • Accepted Date:  28 April 2014
  • Published Online:  05 August 2014

Adaptive stochastic resonance method based on quantum particle swarm optimization

  • 1. State Key Laboratory of Precision Measuring Technology and Instrument, Tianjin University, Tianjin 300072, China;
  • 2. School of Electronic and Information Engineering, Tianjin University, Tianjin 300072, China;
  • 3. International School of Software, Wuhan University, Wuhan 430072, China
Fund Project:  Project supported by the Natural Science Foundation of Tianjin, China (Grant No.13JCYBJC18000).

Abstract: 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.

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