| Summary: | 碩士 === 國立交通大學 === 資訊科學學系 === 83 === We propose three fuzzy neural network models, fuzzy K-nearest
neighbor rule (fuzzy K-NNR) net, two learning steps fuzzy
neural network, and fuzzy functional-link net. The three
fuzzy neural networks are all applied to two important seismic
pattern recognition problems, seismic trace editing and seismic
first arrival picking. The first fuzzy neural network model is
fuzzy K-neighbor rule neural network. Fuzzy K-nearest neighbor
classification rule is implemented by neural network of the
Hamming net. In the training stage of fuzzy K-nearest neighbor
classification rule neural network, each pattern is assigned
fuzzy membership. In the testing stage, testing patterns are
through the neural network to determine which class the testing
pattern belongs to. By adopting fuzzy C-means theorem the
second fuzzy neural network model is two steps learning fuzzy
neural network. The training stage of this neural network
model are divided into two learning steps. The first step is
applying unsupervised learning method using fuzzy C-means
theorem as learning algorithm and second learning step is
perceptron learning by gradient-descent method which is a
supervised learning method. In the testing stage, each testing
pattern is put in this network and transfer the pattern to C
fuzzy degrees, output layer then get the output according to
the C fuzzy degrees. Fuzzy functional-link net is 3rd fuzzy
neural network model incorporated with fuzzy concept in
learning procedure. The three fuzzy neural network are applied
to seismic trace editing and first arrival picking. The
experiments of seismic trace editing and seismic first arrival
picking are quite encouraging.
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