An efficient improved adaptive genetic algorithm for training layered feedforward neural networks
โ Scribed by Wang Xin-miao; Yan Pu-liu; Huang Tian-xi
- Publisher
- Wuhan University
- Year
- 1999
- Tongue
- English
- Weight
- 51 KB
- Volume
- 4
- Category
- Article
- ISSN
- 1007-1202
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
An algorithm is derived for supervised training in mtdtilayer feedforwardneural networks. Relative to the gradient descent backpropagation algorithm it appears to give bothfaster convergence and improved generalization, whilst preserving the system of backpropagating errors throughthe network.
## Abstract Both genetic algorithms (GAs) and artificial neural networks (ANNs) have been used in the field of computational electromagnetics as the most powerful optimizing tools. In this paper, a simple and efficient method is presented to handle the problem of competing convention while training