Training Single Hidden Layer Feedforward Neural Networks by Singular Value Decomposition
Hieu Trung Huynh, Yonggwan Won · 2009
Training neural networks has attracted many researchers for a long time. Many training algorithms and their improvements have been proposed. However, up to now, improving performance of training algorithms for neural networks is still a challenge. In this paper, we investigate a new training method for single hidden layer feedforward neural networks (SLFNs) which use `tansig' activation function. The proposed training algorithm uses SVD (singular value decomposition) to calculate the network parameters. It is simple and has low computational complexity. Experimental results show that the proposed approach can obtain good performance with a compact network which has small number of hidden units.