| Summary: | 碩士 === 國立交通大學 === 資訊科學學系 === 82 === In NN-based recognition systems, the recognition rates
dependent on the quality of feature extraction which is usually
determined by human experts and the models are very complex
because they need multi-layered transformation. Since pattern
recognition is an essential part in many applications,
automating this task becomes more and more important. A neuro-
fuzzy model of adaptive learning and feature detection, called
the fuzzy-filtered neural networks, has been successfully
applied to the problem of plasma spectrum analysis. In this
thesis, we extend the model to another problem, the recognition
of hand-written numerals, to demonstrate its generality. We
proposer 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 and
automatically handle issues of a real-world pattern recognition
problem such as drifting and noise. Simulation results show
that the proposed model is an efficient architecture for
achieving high recognition accuracy.
|
|---|
