Abstract
To improve the generalization performance of RBF networks, samples having been divided into training set and evaluating set, a novel training algorithm is proposed for adjusting the width of center point set based on the standard deviation of evaluating set error. Simulation results show this method is effective in improving the generalization performance of RBF networks. The performance of generalization of RBF network can be remarkably improved by using this training method.
| Original language | English |
|---|---|
| Pages | 859-863 |
| Number of pages | 5 |
| Publication status | Published - 2000 |
| Event | Proceedings of the 3th World Congress on Intelligent Control and Automation - Hefei, China Duration: 28 Jun 2000 → 2 Jul 2000 |
Conference
| Conference | Proceedings of the 3th World Congress on Intelligent Control and Automation |
|---|---|
| Country/Territory | China |
| City | Hefei |
| Period | 28/06/00 → 2/07/00 |
Keywords
- Generalization performance
- Radial basis function networks
- Samples classification
- Width of center point set
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Duan, S., He, C., Xu, L., & Ma, D. (2000). A training method for improving the generalization performance of radial basis function networks. 859-863. Paper presented at Proceedings of the 3th World Congress on Intelligent Control and Automation, Hefei, China.