Fuzzy neural network for invariant optical pattern recognition

James Zhiqing Wen; Pochi Yeh; Xiangyang Yang

doi:10.1117/1.600825

1 August 1996 Fuzzy neural network for invariant optical pattern recognition

James Zhiqing Wen, Pochi Yeh, Xiangyang Yang

Optical Engineering, Vol. 35, Issue 8, (August 1996). https://doi.org/10.1117/1.600825

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.

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James Zhiqing Wen, Pochi Yeh, and Xiangyang Yang "Fuzzy neural network for invariant optical pattern recognition," Optical Engineering 35(8), (1 August 1996). https://doi.org/10.1117/1.600825

Published: 1 August 1996

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