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Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights
Abstract
The authors describe how a two-layer neural network can approximate any nonlinear function by forming a union of piecewise linear segments. A method is given for picking initial weights for the network to decrease training time. The authors have used the method to initialize adaptive weights over a large number of different training problems and have achieved major improvements in learning speed in every case. The improvement is best when a large number of hidden units is used with a complicated desired response. The authors have used the method to train the truck-backer-upper and were able to decrease the training time from about two days to four hours
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Metadata
- Authors
- Nguyen, D., & Widrow, B.
- Venue
- 1990 IJCNN International Joint Conference on Neural Networks
- Publisher
- IEEE
- Published
- 1990
- Pages
- 21-26 vol.3
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2002096058
- Cited by
- 1,375
- References
- 0
- FWCI
- 5.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Nguyen, D., & Widrow, B. (1990). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. 1990 IJCNN International Joint Conference on Neural Networks. https://doi.org/10.1109/ijcnn.1990.137819
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