Liang-Chi Shen

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Well log data inversion is important for the inversion of true formation. There exists a nonlinear mapping between the measured apparent conductivity (C<sub>a</sub>) and the true formation conductivity (C<sub>t</sub>). We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm(More)
Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back(More)
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(More)
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(More)