RIS ID
20668
Abstract
We introduce two new models which are obtained through the modification of the well known methods MLP and cascade correlation. These two methods differ fundamentally as they employ learning techniques and produce network architectures that are not directly comparable. We extended the MLP architecture, and reduced the constructive method to obtain very comparable network architectures. The greatest benefit of these new models is that we can obtain an MLP-structured network through a constructive method based on the cascade correlation algorithm, and that we can train a cascade correlation structured network using the standard MLP learning technique. Additionally, we show that cascade correlation is a universal approximator, a fact that has not yet been discussed in literature.
Publication Details
This paper originally appeared as: Tsoi, AC, Hagenbuchner, M & Micheli, A, Building MLP networks by construction, IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 24-27 July 2000, vol 4, 549-554. Copyright IEEE 2000.