A hybrid neural network for image classification

Ken-Yuh Hsu*, Shiuan-Huei Lin, T. C. Hsieh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Principles of the photorefractive perceptron learning algorithm are described. The influences of the finite response time and hologram erasure of the photorefractive gratings on the convergence property of the photorefractive perceptron learning are discussed. A novel neural network which could resolve these constraints is presented. It is a hybrid system which utilizes the photorefractive holographic gratings to implement the inner product between the input image and the interconnection matrix. A personal computer is used for storing the interconnection matrix and the updating procedure, and it also functions as a feedback means during the learning phase. After training the weight vectors are recorded in the volume hologram of an optical processor. This novel method combines the advantages of the massive parallelism of optical systems and the programmability of electronic computers. Experimental results of image classification are presented. It shows that the system could correctly classify the input patterns into one of the two groups after training on four examples in each group during successive iterations. The system has been extended to perform multi-category image classification.

Original languageEnglish
Pages (from-to)167-183
Number of pages17
JournalOptics and Lasers in Engineering
Volume23
Issue number2-3
DOIs
StatePublished - 1 Jan 1995

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