Performance Comparison of RBF networks and MLPs for Classification

Sug H.

9th WSEAS International Conference on Applied Informatics and Communications, Moscow, Russia, 20 - 22 August 2009, pp.450-452 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Moscow
  • Country: Russia
  • Page Numbers: pp.450-452
  • Istanbul Technical University Affiliated: No


The two well-known neural network algorithms, multilayer perceptrons and radial basis function networks, have different structures and characteristics, so that they have different performance in classification tasks depending on the available training data sets. This paper comparares the performance of the two neural networks with respect to training data set size in classification tasks. Experiments using two real world data sets with the two neural network algorithms show that multilayer perceptrons have relatively better performance for larger data sets and radial basis function networks have relatively better performance for smaller data sets.