The structures of optical neural nets based on new matrix-tensor equivalental models (MTEMs) and results of modeling

Abstract

The structures of optical neural nets (NN) based on new matrix-tensor equivalental models (MTEMs) and algorithms are described in this article. MTE models are neuroparadigm of non-iterative type, which is a generalization of Hopfield and Hamming networks. The adaptive multi-layer networks, auto-associative and hetero-associative memory of 2-D images of high order can be built on the basis of MTEMs. The capacity of such networks in comparison with capacity of Hopfield networks is increased (including capacity for greatly correlated images). The results of modeling show that the number of neurons in neural network MTEMs is 10–20 thousand and more. The problems of training of such networks, different modifications, including networks with double adaptive-equivalental auto-weighing of weights, organization of computing process in different modes of network are discussed. The basic components of networks: matrix-tensor “equivalentors” and variants of their realization on the basis of liquid-crystal structures and optical multipliers with spatial and time integration are considered. The efficiency of proposed optical neural networks on the basis of MTEMs is evaluated for both variants on the level of 109 connections per second. Modified optical connections are realized as liquid-crystal television screens.