Using fuzzy filters as feature detectors

A neuro-fuzzy model of adaptive learning and feature detection is presented. The model, called the fuzzy filtered neural network, was first introduced in a previous publication, which showed its validity in the domain of plasma analysis. Here the authors extend the model to another problem, the reco...

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Bibliographic Details
Main Authors: Chuen-Tsai Sun, Tsuey-Yuh Shuai, Guang-Liang Dai
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Data mining
Detectors
Filtering
Filters
Fuzzy neural networks
Fuzzy systems
Neural networks
Pattern recognition
Sun
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Summary:A neuro-fuzzy model of adaptive learning and feature detection is presented. The model, called the fuzzy filtered neural network, was first introduced in a previous publication, which showed its validity in the domain of plasma analysis. Here the authors extend the model to another problem, the recognition of hand-written numerals, to demonstrate its generality. The authors propose three versions of the architecture, which use one-dimensional fuzzy filters, two-dimensional fuzzy filters, and genetic-algorithm-based fuzzy filters, respectively, as feature detectors. All three versions smoothly handle such issues of a real-world pattern recognition problem as drifting and noise. Simulation results show that the proposed model is an efficient architecture for achieving high recognition accuracy.< >
DOI:10.1109/FUZZY.1994.343752