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Proceedings Paper

Optical neural networks based on wavelet transforms
Author(s): Chunling Fan; Zhihua Jin; Weifeng Tian
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Paper Abstract

The Fourier transform has been widely applied in the optical signal processing, yet it is just fit for analyzing the stationary signals. By extending the Fourier transform into wavelet transform, a new type of filter is proposed and its analogy to neural networks is developed. Optical neural networks (ONNs) are the new type networks, which possess good capacity of super parallel processing, signal transmission and high-density connecting lines. Although neural networks' implementations have been limited by the availability of high-resolution optical devices, by virtue of simple optical architectures for the wavelet transforms, the new neural network is easy to implement in large-scale by applying photoelectric technology. In this paper, the basic principles of ONNs and optical wavelet transform (OWT) are presented respectively, and the principle and structure of their combination—optical neural networks based on the wavelet transform are also proposed. For the optical neural networks and optical wavelet transforms, their optical implementations have many unique superiority, yet theirs combination takes on characteristics better than such structures just using neural networks or wavelet transform. Furthermore, their application perspectives are predicted in the paper.

Paper Details

Date Published: 16 September 2002
PDF: 8 pages
Proc. SPIE 4929, Optical Information Processing Technology, (16 September 2002); doi: 10.1117/12.483237
Show Author Affiliations
Chunling Fan, Shanghai Jiaotong Univ. (China)
Zhihua Jin, Shanghai Jiaotong Univ. (China)
Weifeng Tian, Shanghai Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 4929:
Optical Information Processing Technology
Guoguang Mu; Francis T. S. Yu; Suganda Jutamulia, Editor(s)

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