Abstract
In this paper, a new architecture of divide-and-conquer based radial basis function network (DCRBF) and its learning algorithm are presented. The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which individually takes a sub-input space as its input. The output of this new architecture is a linear combination of the sub-networks' outputs with the coefficients tuned together with each sub-network system parameters. Since this system divides a high-dimensional modelling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net's learning speed as a whole is significantly improved with the comparable generalization ability. We apply DCRBF to model a recurrent version of RBF networks. The experimental results have shown its outstanding performance.
Original language | English |
---|---|
Pages | 513-516 |
Number of pages | 4 |
Publication status | Published - 2003 |
Event | International Joint Conference on Neural Networks 2003 - Portland, OR, United States Duration: 20 Jul 2003 → 24 Jul 2003 |
Conference
Conference | International Joint Conference on Neural Networks 2003 |
---|---|
Country/Territory | United States |
City | Portland, OR |
Period | 20/07/03 → 24/07/03 |
Scopus Subject Areas
- Software
- Artificial Intelligence