Enhancement of stochastic resonance with tuning noise and system parameters

Xingxing Wu, Zhong Ping Jiang, Daniel W. Repperger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Stochastic resonance has been increasingly used for signal estimation, signal transmission, signal detection and image processing. The stochastic resonance effect can be realized by tuning system parameters or by adding noise. In our recent paper, we have investigated the possibility to enhance the aperiodic stochastic resonance (ASR) effect by tuning system parameters and adding noise simultaneously for the Gaussian-distribution weak input signal. This paper extends the result to a more general case using standard optimization theory. It is shown that the normalized power norm of the bistable double-well system with a small input signal can reach a larger maximal value by this scheme. An on-line fast-converging optimization algorithm is also proposed for searching the optimal values of system parameters and noise intensity.

Original languageEnglish (US)
Title of host publicationProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages1823-1827
Number of pages5
DOIs
StatePublished - 2006
Event6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China
Duration: Jun 21 2006Jun 23 2006

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume1

Other

Other6th World Congress on Intelligent Control and Automation, WCICA 2006
Country/TerritoryChina
CityDalian
Period6/21/066/23/06

Keywords

  • Optimization
  • Signal processing
  • Stochastic resonance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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