標題: GENERALIZED PERCEPTRON LEARNING RULE AND ITS IMPLICATIONS FOR PHOTOREFRACTIVE NEURAL NETWORKS
作者: CHENG, CJ
YEH, PC
HSU, KY
交大名義發表
光電工程學系
National Chiao Tung University
Department of Photonics
公開日期: 1-九月-1994
摘要: We consider the properties of a generalized perceptron learning network, taking into account the decay or the gain of the weight vector during the training stages. A mathematical proof is given that shows the conditional convergence of the learning algorithm. The analytical result indicates that the upper bound of the training steps is dependent on the gain (or decay) factor. A sufficient condition of exposure time for convergence of a photorefractive perceptron network is derived. We also describe a modified learning algorithm that provides a solution to the problem of weight vector decay in an optical perceptron caused by hologram erasure. Both analytical and simulation results are presented and discussed.
URI: http://dx.doi.org/10.1364/JOSAB.11.001619
http://hdl.handle.net/11536/2363
ISSN: 0740-3224
DOI: 10.1364/JOSAB.11.001619
期刊: JOURNAL OF THE OPTICAL SOCIETY OF AMERICA B-OPTICAL PHYSICS
Volume: 11
Issue: 9
起始頁: 1619
結束頁: 1624
顯示於類別:期刊論文


文件中的檔案:

  1. A1994PE85300011.pdf