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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ninomiya, H. Kinoshita, N. |
| Copyright Year | 1999 |
| Description | Author affiliation: Dept. of Inf. Sci., Shonan Inst. of Technol., Fujisawa, Japan (Ninomiya, H.) |
| Abstract | This paper describes a new supervised learning algorithm for multilayer neural networks without explicit error backpropagation (BP). The proposed method allows the asynchronous and parallel processing by neurons. Therefore this algorithm has an advantage over the standard backpropagation algorithm in hardware implementation of trainable artificial neural networks. We demonstrate the validity of the method through computer simulations. It is shown that the algorithm is not only almost equivalent to the BP algorithm from the viewpoint of the generalization ability, but also much superior to the one from the viewpoint of the convergence speed. As a result, it is confirmed that our algorithm is efficient and practical for the supervised learning of multilayer neural networks. |
| Starting Page | 1389 |
| Ending Page | 1392 |
| File Size | 335348 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780355296 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.1999.831166 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-07-10 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Backpropagation algorithms Multi-layer neural network Supervised learning Artificial neural networks Neural networks Computer errors Parallel processing Neurons Neural network hardware Computer simulation |
| Content Type | Text |
| Resource Type | Article |
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