Xiangyang Yang

Optical neural network with pocket-sized liquid-crystal televisions.

A compact optical neural network that uses high-resolution liquid-crystal televisions has been constructed. System design considerations and an experimental demonstration of the liquid-crystal television neural network are reported.

Compact optical neural network using cascaded liquid crystal television.

An optical neural network using two tightly cascaded liquid crystal televisions is presented. This new optical architecture offers compactness in size, ease of alignment, higher light efficiency, better image quality, and low cost. The implementation of the autoassociative and heteroassociative memories is given.

Optical implementation of the Hamming net

We present an optical implementation of the Hamming net that can be used as an optimum image classifier or an associative memory. We introduce a modified Hamming net, in which the dynamic range requirement of the spatial light modulator can be relaxed and the number of iteration cycles in the second layer (or maxnet) can be reduced. Experimental demonstrations of the optical implementation of the Hamming net are also given.

Mirror-array optical interconnected neural network.

A high-light-efficiency optical neural network that uses a mirror-array interconnection is proposed. Design considerations for the mirror array and experimental demonstration are given.

Redundant-interconnection interpattern-association neural network

We have shown that introducing interconnection redundancy can make a neural network more robust. We describe performances under noisy input and partial input that show that the optimum-redundant interconnection improves both the noise tolerance and the pattern discriminability. Simulated and experimental demonstrations are also provided.

IMPLEMENTATION OF A POLYCHROMATIC HAMMING NET FOR COLOR IMAGE CLASSIFICATION

A color exemplar-based Hamming net as applied to a color image classifier is presented. The color decomposition and composition techniques for constructing a polychromatic Hamming layer and winner-take-all (WTA) layer interconnection weight matrix (IWM) are discussed. We have simplified the WTA algorithm to reduce the number of iteration cycles in the WTA layer. To confirm the operation of the color Hamming net we provide simulation and experimental demonstrations.

Exemplar-based optical neural net classifier for color pattern recognition

We present a color exemplar-based neural network that can be used as an optimum image classifier or an associative memory. Color decomposition and composition technique is used for constructing the polychromatic interconnection weight matrix (IWM). The Hamming net algorithm is modified to relax the dynamic range requirement of the spatial light modulator and to reduce the number of iteration cycles in the winner-take-all layer. Computer simulation results demonstrated the feasibility of this approach

Multicolor neural network using cascaded color LCTVs

One of the important aspects of a neural network is the exploration of the spatial content of the object under observation in this connection. We shall present a color optical neural network using inexpensive package-size liquid crystal televisions (LCTVs). By introducing a color encoding technique in conjunction with a conventional white light source, a multicolor neural net is synthesized. We have shown that by exploiting the spectral component of the LCTVs storage capacity of the neural net can be improved. Simulations as well as experimental results obtained from the proposed LCTV color neural net are provided, in which the effects due to noise and due to color crosstalk are addressed.

Polychromatic neural networks

A polychromatic optical neural network using cascaded liquid crystal televisions (LCTV) that uses a polychromatic interconnection weight matrix (IWM) for color pattern recognition is presented and simulated. Extension of the polychromatic neural net for multichannel operation is proposed.

Hetero-association for pattern translation

A hetero-association neural network using an interpattern association algorithm is presented. By using simple logical rules, hetero-association memory can be constructed based on the association between the input-output reference patterns. For optical implementation, a compact size liquid crystal television neural network is used. Translations between the English letters and the Chinese characters as well as Arabic and Chinese numerics are demonstrated. The authors have shown that the hetero-association model can perform more effectively in comparison to the Hopfield model in retrieving large numbers of similar patterns.