Abstract
In this paper, an investigation into the performance of multi-layered Radial Basis Functions(RBF) networks is conducted which use Gaussian function in place of sigmoidal function in multi-layered Neural Networks(NNs). The focus is on the difference of approximation abilities between multi-layered RBF networks and NNs. A function approximation problem is employed to evaluate the performance of multi-layered RBF networks, and several types of different functions are used as the functions to be approximated. Gradient method is employed to optimize the parameters including centers, widths, and linear connection weights to the output nodes. It is shown from the result that RBF does not always have significant advantages over sigmoidal functions when they are used in multi-layered networks.
| Original language | English |
|---|---|
| Pages (from-to) | 908-911 |
| Number of pages | 4 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 1 |
| Publication status | Published - Dec 1 1997 |
| Event | Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5) - Orlando, FL, USA Duration: Oct 12 1997 → Oct 15 1997 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Hardware and Architecture