Optical implementation of an associative neural network model with a stochastic process
Jun Ohta, Shuichi Tai, Masaya Oita, Kenichi Kuroda, Kazuo Kyuma, Koichi Hamanaka · Applied Optics · 1989
An optical associative neural network with a stochastic thresholding procedure has been demonstrated. The use of stochastic processing drastically improved the convergence rate into the correct global minima (recognition rate). The properties of undesirable spurious minima were also investigated. It was found that the spurious minima were represented as the mixed states of the stored vectors. A useful method to estimate the required noise level to vanish the spurious minima is described.