The performance of the backpropagation algorithm with varying slope of the activation function
Some adaptations are proposed to the basic BP algorithm in order to provide an efficient method to non-linear data learning and prediction. In this paper, an adopted BP algorithm with varying slope of activation function and different learning rates is put forward. The results of experiment indicate...
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| Published in: | Chaos, solitons and fractals Vol. 40; no. 1; pp. 69 - 77 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
15.04.2009
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| ISSN: | 0960-0779, 1873-2887 |
| Online Access: | Get full text |
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| Summary: | Some adaptations are proposed to the basic BP algorithm in order to provide an efficient method to non-linear data learning and prediction. In this paper, an adopted BP algorithm with varying slope of activation function and different learning rates is put forward. The results of experiment indicated that this algorithm can get very good performance of training. We also test the prediction performance of our adopted BP algorithm on 16 instances. We compared the test results to the ones of the BP algorithm with gradient descent momentum and an adaptive learning rate. The results indicate this adopted BP algorithm gives best performance (100%) for test example, which conclude this adopted BP algorithm produces a smoothed reconstruction that learns better to new prediction function values than the BP algorithm improved with momentum. |
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| ISSN: | 0960-0779 1873-2887 |
| DOI: | 10.1016/j.chaos.2007.07.033 |