Conference Paper

Enhancement of stochastic resonance by tuning system parameters and adding noise simultaneously

Dept. of Electr. and Comput. Eng., Polytech. Univ. of Brooklyn, NY
DOI: 10.1109/ACC.2006.1657196 Conference: American Control Conference, 2006
Source: IEEE Xplore

ABSTRACT The stochastic resonance effect can be realized by tuning system parameters or by adding noise. This paper investigates the possibility to enhance the stochastic resonance effect by tuning system parameters and adding noise simultaneously. First, we use examples to demonstrate the situation where only the system parameters or noise can be adjusted to maximize the stochastic resonance effect. Then, it is shown, using standard optimization theory, that the normalized power norm of the bistable double-well system with aperiodic input signal can reach a larger maximal value by tuning the system parameter and adding noise simultaneously. Finally, for the purpose of practical implementation, searching for the optimal system parameter and noise intensity is realized by an on-line fast-converging optimization algorithm

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    ABSTRACT: The traditional stochastic resonance is realized by adding an optimal amount of noise, while the parameter-tuning stochastic resonance is realized by optimally tuning the system parameters. This paper reveals the possibility to further enhance the stochastic resonance effect by tuning system parameters and adding noise at the same time using optimization theory. The further improvement of the maximal normalized power norm of the bistable double-well dynamic system with white Gaussian noise input can be converted to an optimization problem with constraints on system parameters and noise intensity, which is proven to have one and only one local maximum for the Gaussian-distributed weak input signal. This result is then extended to the arbitrary weak input signal case. For the purpose of practical implementation, a fast-converging optimization algorithm to search the optimal system parameters and noise intensity is also proposed. Finally, computer simulations are performed to verify its validity and demonstrate its potential applications in signal processing.
    01/2006; DOI:10.4310/CIS.2006.v6.n1.a1
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    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
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    ABSTRACT: Stochastic resonance (SR) is a phenomenon that performance of the nonlinear system can be improved with the addition of optimal amount of noise. Stochastic resonance has been increasingly used for signal processing. The output of the nonlinear bistable dynamic system with white Gaussian noise input can be used to restore the weak input signal, if the similarity between the input signal and the output can be maximized. This paper first use the optimization theory to show that the normalized power norm describing the similarity will reach a larger maximum when tuning both system parameters and noise intensity, compared with that of only adjusting noise intensity (classical stochastic resonance) or only adjusting system parameters. Then, computer simulations are performed to verify this proposal and demonstrate its application in signal processing
    Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference on; 07/2006

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