In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.
Intelligent Systems and Automation: 2nd Mediterranean Conference on Intelligent Systems and Automation (CISA'09)
- Pub Date:
- March 2009
- Neural networks;
- Neural networks fuzzy logic artificial intelligence;
- Acoustic imaging displays pattern recognition feature extraction