Abstract
How to determine the appropriate or optimal activation function in the neural networks for a specific learning samples remains open. In this paper the cascade-correlation algorithm which is an efficient constructive algorithm is used after implementation of some kinds of clustering algorithms to produce a modular network structure as a surrogate of activation node functions in the radial basis function (RBF) networks. In this way great improvement on the convergence rate of training algorithms and better approximation are achieved. Simulations with the two-spiral data sets proved the above assertion.
| Original language | English |
|---|---|
| Pages | 1177-1182 |
| Number of pages | 6 |
| Publication status | Published - 2000 Dec 1 |
| Event | 26th Annual Conference of the IEEE Electronics Society IECON 2000 - Nagoya, Japan Duration: 2000 Oct 22 → 2000 Oct 28 |
Conference
| Conference | 26th Annual Conference of the IEEE Electronics Society IECON 2000 |
|---|---|
| Country | Japan |
| City | Nagoya |
| Period | 00/10/22 → 00/10/28 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
Fingerprint Dive into the research topics of 'Determination of the appropriate node function of NNs by using the cascade-correlation algorithms'. Together they form a unique fingerprint.
Cite this
Wan, W., Hirasawa, K., Murata, J., Jin, C., & Hu, J. (2000). Determination of the appropriate node function of NNs by using the cascade-correlation algorithms. 1177-1182. Paper presented at 26th Annual Conference of the IEEE Electronics Society IECON 2000, Nagoya, Japan.