The Study of Fuzzy Neural Network For Seismic Pattern Recognition

• Main Authors: Yune-Wei Yuan, 袁永偉
• Other Authors: Kou-Yuan Huang
• Format: Others
• Language: en_US
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/66437905893802521127

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.