A Network Pruning Algorithm for Combined Function and Derivative Approximation Conference Paper uri icon


  • This paper describes newly discovered types of overfitting that occur when simultaneously fitting a function and its first derivatives with multilayer feedforward neural networks. We analyze the overfitting and demonstrate how it develops. These types of overfitting occur over very narrow regions in the input space, thus a validation set is not helpful in detecting them. A new pruning algorithm is proposed to eliminate these types of overfitting. Simulation results show that the pruning algorithm successfully eliminates the overfitting, produces smooth responses and provides excellent generalization capabilities. The proposed pruning algorithm can be used with any single-output, two-layer network, which uses a hyperbolic tangent transfer function in the hidden layer. 2009 IEEE.

name of conference

  • 2009 International Joint Conference on Neural Networks

published proceedings

  • 2009 International Joint Conference on Neural Networks

author list (cited authors)

  • Pukrittayakamee, A., Hagan, M., Raff, L., Bukkapatnam, S., & Komanduri, R.

citation count

  • 0

complete list of authors

  • Pukrittayakamee, Arjpolson||Hagan, Martin||Raff, Lionel||Bukkapatnam, Satish||Komanduri, Ranga

publication date

  • January 2009