Dynamic tunneling based regularization in feedforward neural networks
This paper presents a new regularization method based on dynamic tunneling for enhancing generalization capability of multilayered neural networks. The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling. Undesired sub-o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2001
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2685/ http://shdl.mmu.edu.my/2685/1/1926.pdf |
| Summary: | This paper presents a new regularization method based on dynamic tunneling for enhancing generalization capability of multilayered neural networks. The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling. Undesired sub-optimal solutions may be increased or introduced from regularized objective function. Hence, the proposed method is capable of enhancing the regularization property without getting stuck at sub-optimal values in search space. The regularization property and escape from the sub-optimal values have been demonstrated through computer simulations on two examples. (C) 2001 Elsevier Science B.V. All rights reserved. |
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