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Feedforward neural networks (FNNs) with a single hidden layer have been widely applied in data modeling due to its’ universal approximation capability to nonlinear maps. However, such a theoretical result does not provide with any guideline to determine the architecture of the model in practice. Thus, researches on self-organization of FNNs are useful and critical for effective data modeling. This...
When a sigmoidal feedforward neural network (SFNN) is trained by the gradient-based algorithms, the quality of the overall learning process strongly depends on the initial weights. To improve the algorithm stability and avoid local minima, a Mutual Information based weight initialization (MIWI) method is proposed for SFNN. The useful information contained in input variables is measured with the mutual...
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