Function approximation by complex-valued multilayer perceptron with stochastic resonance

This paper explores complex-valued multilayer perceptrons (MLPs) with the mechanism of stochastic resonance (SR). SR is a phenomenon such that a weak periodic signal in the system can be enhanced and detected in the presence of noises. It is expected that the combination of complex-valued encoding and SR mechanism will improve the performance of MLPs, rather than MLPs with either of them. The performances are evaluated through approximations for one- and two-dimensional functions. It is shown that complex-valued MLP with SR could achieve more precise approximations rather than conventional real-valued MLP and complex-valued MLP without SR mechanism.