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