Browsing by Author "You, Jiun-Der"

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Hough transform neural network for pattern detection and seismic applications
(2008-10-01)
Huang, Kou-Yuan
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Chen, Kai-Ju
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You, Jiun-Der
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Tung, An-Ching
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資訊工程學系
Hough transform neural network is adopted to detect the line pattern of direct wave and the hyperbolic pattern of reflection wave in a one-shot seismogram. We use time difference from point to hyperbola and line as the distance in the pattern detection of seismic direct and reflection waves. This distance calculation makes the parameter learning feasible. One set of parameters represents one pattern. Many sets of parameters represent many patterns. The neural network can calculate the distances from point to many patterns as total error. The parameter learning rule is derived by gradient descent method to minimize the total error. The network is applied to three kinds of data in the experiments. One is the line and hyperbolic pattern in the image data. The second is the simulated one-shot seismic data. And the last is the real one-shot seismic data. Experimental results show that lines and hyperbolas can be detected correctly in three kinds of data. The method can also tolerate certain level of noise data. The detection results in the one-shot seismogram can improve the seismic interpretation and further seismic data processing. (C) 2008 Elsevier B.V. All rights reserved.
PROOF OF HIDDEN NODE NUMBER AND EXPERIMENTS ON RBF NETWORK FOR WELL LOG DATA INVERSION
(2016)
Huang, Kou-Yuan
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Shen, Liang-Chi
;
You, Jiun-Der
;
Weng, Li-Sheng
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資訊工程學系
In the multilayer perceptron (MLP), there was a theorem about the maximum number of separable regions (M) given the number of hidden nodes (H) in the input d-dimensional space. We propose a recurrence relation to prove the theorem using the expansion of recurrence relation instead of proof by induction. We use three-layer radial basis function net (RBF) on the well log data inversion to test the number of hidden nodes determined by the theorem. The three- layer RBF has more nonlinear mapping. In the experiments, we have 31 simulated well log data. 25 well log data are used for training, and 6 are for testing. The experimental results can support the number of hidden nodes determined by the theorem.
Proof of Hidden Node Number in MLP and Experiments on Well Log Data Inversion
(2015)
Huang, Kou-Yuan
;
Shen, Liang-Chi
;
You, Jiun-Der
;
資訊工程學系
;
Department of Computer Science
In the multilayer perceptron (MLP), there was a theorem about the maximum number of separable regions (M) given the number of hidden nodes (H) in the input d-dimensional space. We propose a recurrence relation in the high dimensional space and prove the theorem using the expansion of recurrence relation instead of proof by induction. The MLP model has input layer, one hidden layer, and output layer. We use different MLP models on the well log data inversion to test the number of hidden nodes determined by the theorem. The inputs are the first order, second order, and third order features. Higher order neural network (HONN) has the property of more nonlinear mapping. In the experiments, we have 31 simulated well log data. 25 well log data are used for training, and 6 are for testing. The experimental results can support the number of hidden nodes determined by the theorem.