Adaptive stochastic resonance

Author

Mitaim, Sanya ; Kosko, Bart

Author_Institution

Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA

Volume

86

Issue

11

fYear

1998

fDate

11/1/1998 12:00:00 AM

Firstpage

2152

Lastpage

2183

Abstract

This paper shows how adaptive systems can learn to add an optimal amount of noise to some nonlinear feedback systems. This “stochastic resonance” (SR) effect occurs in a wide range of physical and biological systems. The noise energy can enhance the faint periodic signals or faint broadband signals that force the dynamical systems. Fuzzy and other adaptive systems can learn to induce SR based only on samples from the process. The paper derives the SR optimality conditions that any stochastic learning system should try to achieve. The adaptive system learns the SR effect as the system performs a stochastic gradient ascent on the signal-to-noise ratio. The stochastic learning scheme does not depend on a fuzzy system or any other adaptive system. Simulations test this SR learning scheme on the popular quartic-bistable dynamical system and on other dynamical systems. The driving noise types range from Gaussian white noise to impulsive noise to chaotic noise

Keywords

adaptive systems; function approximation; fuzzy systems; learning systems; neural nets; noise; nonlinear dynamical systems; signal processing; Gaussian white noise; adaptive stochastic resonance; adaptive systems; chaotic noise; feedback; function approximation; fuzzy system; impulsive noise; neural networks; noise energy; nonlinear dynamical systems; stochastic learning system; Adaptive systems; Biological systems; Feedback; Fuzzy systems; Gaussian noise; Learning systems; Signal to noise ratio; Stochastic resonance; Stochastic systems; Strontium;

fLanguage

English

Journal_Title

Proceedings of the IEEE

Publisher

ieee

ISSN

0018-9219

Type

jour

DOI

10.1109/5.726785

Filename

726785