Performance analysis of complex-valued neural networks with stochastic resonance

In this paper, we explore a complex-valued neural networks (NNs) with incorporating the mechanism of stochastic resonance (SR). SR is a phenomenon where weak periodic signals in the system can be detected by the presence of the noise. It is expected that the combination of complex-valued and SR mechanism will improve the performance of NNs, rather than NNs with either of them. The performances are evaluated through the problem of affine transformations in two-dimensional space. It is shown that complex-valued NN with SR could achieve more precise transformations rather than conventional real-valued NN and complex-valued NN.