Adaptive stochastic resonance and fuzzy approximation

The paper derives the stochastic resonance (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. The learning process is slow and noisy and can require heavy computation. Robust noise suppressors can improve the learning process when we can estimate the impulsiveness of the noise or of other learning terms. Simulations test this SR learning scheme on the popular quartic-bistable dynamical system for many types of noise. The simulation test results suggest that fuzzy techniques and perhaps other “intelligent” techniques can induce SR in many cases when users cannot state the exact form of the dynamical systems