MLP in layer-wise form with applications to weight decay
Neural Computation
Proceedings of the Second European Workshop on Genetic Programming
Discovering efficient learning rules for feedforward neural networks using genetic programming
Recent advances in intelligent paradigms and applications
An improved BP algorithm based on global revision factor and its application to PID control
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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This paper analyzes the effect of data-contrast to a backpropagation (BP) network and introduces a data preprocessing algorithm that can improve the efficiency of the standard BP learning. The basic idea is to transform input data to a range that associates the high-slope region of the sigmoid function where a relatively large modification of weights occurs. A simple uniform transformation to such a desired range, however, can lead to a slow and unbalanced learning if the data distribution is heavily skewed. To facilitate data processing on such distribution, the authors propose a modified histogram equalization technique which enhances the sparing between the data points in the heavily concentrated regions of the distribution