In this paper, we propose the backpropagation algorithm to train online an evolving radial basis function. Structure and parameter learning are updated at the same time in our algorithm, we do not make difference in structure learning and parameter learning. It generate groups with an online clustering. The center is updated in order to get that the center is near to the incoming data in each iteration, in this way, It does not need to generate a new rule in each iteration, i.e., it does not generate many rules and It does not need to prune the rules. We give a time varying learning rate for backpropagation training in the parameters.
|Original language||American English|
|Number of pages||3|
|State||Published - 1 Dec 2010|
|Event||conference - |
Duration: 1 Dec 2010 → …
|Period||1/12/10 → …|
De Jesus Rubio, J., Pacheco, J., & Rivera, R. (2010). Proceedings of the International Symposium on Evolving Intelligent Systems - A Symposium at the AISB 2010 Convention. 23-25. Paper presented at conference, . https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84863933136&origin=inward