Widrow-cellular neural network and optoelectronic implementation

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Abstract:

A new type of optoelectronic cellular neural network has been developed by providing the capability of coefficients adjusment of cellular neural network (CNN) using Widrow based perceptron learning algorithm. The new supervised cellular neural network is called Widrow-CNN. Despite the unsupervised CNN, the proposed learning algorithm allows to use the Widrow-CNN for various image processing applications easily. Also, the capability of CNN for image processing and feature extraction has been improved using basic joint transform correlation architecture. This hardware application presents high speed processing capability compared to digital applications. The optoelectronic Widrow-CNN has been tested for classic CNN feature extraction problems. It yields the best results even in case of hard feature extraction problems such as diagonal line detection and vertical line determination.

Document Type: Research Article

DOI: http://dx.doi.org/10.1078/0030-4026-00366

Affiliations: Department of Electrical Engineering, Yildiz Technical University, Besiktas, Istanbul, 34349, Turkey

Publication date: September 1, 2004

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