Robust pruning of RBF network for neural tracking control systems

Ni, J. and Song, Q. and Grimble, M.J. (2006) Robust pruning of RBF network for neural tracking control systems. In: 45th IEEE Conference on Decision and Control, 2006-12-13 - 2006-12-15. (https://doi.org/10.1109/CDC.2006.377039)

Abstract

It is difficult to determine the number of nodes that should be used in a neural network. An adaptive method is proposed whereby the initial select is based on the largest expected number and the algorithm then "prunes" the numbers. A robust backpropagation training algorithm is proposed for the online tuning of a radial basis function(RBF) network tracking control system. The structure of the RBF network controller is derived using a filtered error approach. The proposed pruning method in this paper begins with a relatively large network, and certain neural units of the RBF network are dropped by examining the estimation error increment. A complete convergence proof is provided in the presence of disturbances.

• Share and Export
  

  ReferAtomJSONOpenURL ContextObject in SpanMPEG-21 DIDLRDF+XMLRDF+N-TriplesRefWorksHTML CitationEndNoteMultiline CSVRIS (EndNote Online, Zotero, Ref manager, etc)Data Cite XMLDublin CoreEP3 XMLASCII CitationRDF+N3METSSimple MetadataRIOXX2 XMLBibTeXOpenURL ContextObjectMODS
• Item metadata

  Item type:	Conference or Workshop Item(Paper)
  ID code:	37713
  Dates:	Date Event 2006 Published
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering
  Depositing user:	Pure Administrator
  Date deposited:	17 Feb 2012 12:33
  Last modified:	11 Nov 2024 16:19
  URI:	https://strathprints.strath.ac.uk/id/eprint/37713