A new learning method using prior information of neural networks |
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Authors: | Baiquan Lu Kotaro Hirasawa Junichi Murata |
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Affiliation: | (1) Department of Electrical and Electronic Systems Engineering, Graduate School of Information Science and Electrical Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-Ku, 812-8581 Fukuoka, Japan |
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Abstract: | In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural
networks are used for identification of systems, all of their weights are trained independently, without considering interrelated
weights values. Thus, the training results are usually not good. The reason for this in that each parameter has its influence
on others during learning. To overcome this problem, we first give an exact mathematical equation that describes the relation
between weight values given a set of data conveying prior information. The we present a new learning method that trains part
of the weights and calculates the others using these exact mathematical equations. This method often a priori keeps the given
mathematical structure exactly the same during learning; in other words, training is done so that the network follows a predetermined
trajectory. Numerical computer simulation results are provided to support this approach.
This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January
19–22, 1999. |
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Keywords: | Prior information Neural network learning Part parameter learning Exact mathematical structure |
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