Browsing by Author "Weng, Li-Sheng"

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PROOF OF HIDDEN NODE NUMBER AND EXPERIMENTS ON RBF NETWORK FOR WELL LOG DATA INVERSION
(2016)
Huang, Kou-Yuan
;
Shen, Liang-Chi
;
You, Jiun-Der
;
Weng, Li-Sheng
;
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.
Radial Basis Function Network for Well Log Data Inversion
(2011)
Huang, Kou-Yuan
;
Shen, Liang-Chi
;
Weng, Li-Sheng
;
資訊工程學系
;
Department of Computer Science
We adopt the radial basis function network (RBF) for well log data inversion. We propose the 3 layers RBF. Inside RBF, the 1-layer perceptron is replaced by 2-layer perceptron. It can do more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at 2-layer perceptron. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). 25 simulated well log data are used in the training. From experimental results, the network with 10 input data, first layer with 27 nodes, second layer with 9 hidden nodes and 10 output nodes can get the smallest average mean absolute error in the training. After training in the network, we apply it to do the inversion of the real field well log data to get the inverted Ct. Result is good. It shows that the RBF can do the well log data inversion.
WELL LOG DATA INVERSION USING RADIAL BASIS FUNCTION NETWORK
(2011)
Huang, Kou-Yuan
;
Shen, Liang-Chi
;
Weng, Li-Sheng
;
資訊工程學系
;
Department of Computer Science
We use the radial basis function network (RBF) for well log data inversion. The first step of the network is the K-means clustering. For the second step, we adopt the 2-layer perceptron instead of conventional 1-layer perceptron. The 2-layer perceptron can do the more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at the second step. The input of the network is the apparent conductivity (Ca) and the output of the network is the true formation conductivity (Ct). The original features are the network input for training process. According to our experimental results, the three-layer radial basis function can get smaller error between the desired output and the actual output. The network with 10 input features, first layer with 27 nodes, second layer with 10 hidden node, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. After simulation, we apply the network to the real field data. The result is good. It shows that the RBF can do the well log data inversion.