The most promising approaches for optical neural networks are based on intensity encoding However a serious drawback of intensity encoding is the lack of negative values and optical subtraction which are essential for rendering neural networks useful To overcome the need for optical subtraction a novel training method is described here that is es pecially useful for optical multilayer neural ne...

in this paper, the application of neural networks for simulation and optimization of the cogeneration systems has been presented. cgam problem, a benchmark in cogeneration systems, is chosen as a casestudy. thermodynamic model includes precise modeling of the whole plant. for simulation of the steadysate behavior, the static neural network is applied. then using dynamic neural network, plant is...

We propose a novel application of sBs in embedded optical fibers as a building block for the implementation of optical neural net based on smart structures. By employing well established technology in fiber embedding in structures, and exploiting the ability of sBs as a highly versatile ambient sensor, and its nonlinear optical property of energy addition and subtraction to perform optical arit...

A new associative memory neural network which can be constructed using optical matched filters is described. It has three layers, the centre one being iterative with its weights set prior to training. The other two layers are feedforward nets and the weights are set during training. The best choice of central layer weights, or in optical terms, of pairs of images associated in a hologram is con...

This paper, written for interdisciplinary audience, presents computational image reconstruction implementable by quantum optics. The input-triggered selection of a high-resolution image among many stored ones, and its reconstruction if the input is occluded or noisy, has been successfully simulated. The original algorithm, based on the Hopfield associative neural net, was transformed in order t...