Bio-inspired Artificial Intelligence: А Generalized Net Model of the Regularization Process in MLP
Many objects and processes inspired by the nature have been recreated by the scientists. The inspiration to create a Multilayer Neural Network came from human brain as member of the group. It possesses complicated structure and it is difficult to recreate, because of the existence of too many proces...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Bulgarian Academy of Sciences
2013-10-01
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| Series: | International Journal Bioautomation |
| Subjects: | |
| Online Access: | http://www.biomed.bas.bg/bioautomation/2013/vol_17.3/files/17.3_02.pdf |
| Summary: | Many objects and processes inspired by the nature have been recreated by the scientists. The inspiration to create a Multilayer Neural Network came from human brain as member of the group. It possesses complicated structure and it is difficult to recreate, because of the existence of too many processes that require different solving methods. The aim of the following paper is to describe one of the methods that improve learning process of Artificial Neural Network. The proposed generalized net method presents Regularization process in Multilayer Neural Network. The purpose of verification is to protect the neural network from overfitting. The regularization is commonly used in neural network training process. Many methods of verification are present, the subject of interest is the one known as Regularization. It contains function in order to set weights and biases with smaller values to protect from overfitting. |
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| ISSN: | 1314-1902 1314-2321 |