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Optical Engineering

Fuzzy neural network for invariant optical pattern recognition
Author(s): James Zhiqing Wen; Pochi Yeh; Xiangyang Yang
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Paper Abstract

A novel fuzzy neural network (FNN) model for invariant pattern recognition is presented that combines fuzzy set reasoning and artificial neural network techniques. The presented FNN consists of three blocks: fuzzifier, fuzzy perceptron, and defuzzifier. It fuzzifies the input patterns and trains the interconnection weights according to membership functions instead of traditional binary values. The proposed FNN has been applied to 2-D binary-image pattern recognition under shift and some other types of distortions. In comparison with the classical multilayer perceptron, the FNN possesses a higher recognition rate and is more robust to input distortions.

Paper Details

Date Published: 1 August 1996
PDF: 8 pages
Opt. Eng. 35(8) doi: 10.1117/1.600825
Published in: Optical Engineering Volume 35, Issue 8
Show Author Affiliations
James Zhiqing Wen, Univ. of New Orleans (United States)
Pochi Yeh, Univ. of California/Santa Barbara (United States)
Xiangyang Yang, Univ. of New Orleans (United States)

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