Optical implementation of the Hopfield neural network using multiple fiber nets

Optical implementation of the hopfield neural network with matrix gratings

We propose a new method for the optical implementation of the Hopfield neural network with a universal tool. The tool is a matrix grating constituted with a group of element gratings. The algorithms for designing a matrix grating are proposed, and a matrix grating is created to execute recognition experiments by use of the Hopfield neural network. The experimental results demonstrate that the proposed method performs well. The stability of the light efficiencies of different optical components used in optical networks is also considered.

Optical interconnection for neural networks by use of a self-imaging function

A grating-lens combination unit is developed to form a scalingself-transform function that can self-image on scale. Then an arrayof many such grating-lens units is used for the optical interconnectionof a two-dimensional neural network, and experiments are carriedout. We find that our idea is feasible, the optical interconnectionsystem is simple, and optical adjustment is easy.

Reversal-input superposing technique for all-optical neural networks

The proposed technique for optical neural networks can perform all the neural operations in a positive range. Bipolar weights of the neurons are represented by unipolar weights with a positive constant. By superposing the reversal inputs to the weighted sums, we can perform subtraction in a neuron by the nonlinear output function with a negative offset constant. This means that the number of processing elements needed in the proposed system is the same as that of neurons in the original neural network model. An experimental neural system is demonstrated for verification of this technique. The Hopfield model is adapted as an example of the neural networks implemented in the experimental neural system.

Design and performance optimization of fiber optic adaptive filters

There is a great need for easy-to-fabricate and versatile fiber optic signal processing systems in which optical fibers are used for the delay and storage of wideband guided lightwave signals. We describe the design of the least-mean-square algorithm-based fiber optic adaptive filters for processing guided lightwave signals in real time. Fiber optic adaptive filters can learn to change their parameters or to process a set of characteristics of the input signal. In our realization we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in the processing speed, parallelism, and interconnection. Many schemes for optical adaptive filtering of electronic signals are available in the literature. The new optical adaptive filters described in this paper are for optical processing of guided lightwave signals, not electronic signals. We analyzed the convergence or learning characteristics of the adaptive filtering process as a function of the filter parameters and the fiber optic hardware errors. From this analysis we found that the effects of the optical round-off errors and noise can be reduced, and the learning speed can be comparatively increased in our design through an optimal selection of the filter parameters. A general knowledge of the fiber optic hardware, the statistics of the lightwave signal, and the desired goal of the adaptive processing are enough for this optimum selection of the parameters. Detailed computer simulations validate the theoretical results of performance optimization.