Nonlinear system modeling via knot-optimizing B-spline networks

Ka Fai Cedric Yiu, Song Wang, Kok Lay Teo, Ah Chung Tsoi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


In using the B-spline network for nonlinear system modeling, owing to a lack of suitable theoretical results, it is quite difficult to choose an appropriate set of knot points to achieve a good network structure for minimizing, say, a minimum error criterion. In this paper, a novel knot-optimizing B-spline network is proposed to approximate general nonlinear system behavior. The knot points are considered to be independent variables in the B-spline network and are optimized together with the B-spline expansion coefficients. A simulated annealing algorithm with an appropriate search strategy is used as an optimization algorithm for the training process in order to avoid any possible local minima. Examples involving dynamic systems up to six dimensions in the input space to the network are solved by the proposed method to illustrate the effectiveness of this approach.
Original languageEnglish
Pages (from-to)1013-1022
Number of pages10
JournalIEEE Transactions on Neural Networks
Issue number5
Publication statusPublished - 1 Sept 2001


  • B-splines
  • Knot points
  • Neural network
  • Nonlinear system modeling

ASJC Scopus subject areas

  • Software
  • General Medicine
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


Dive into the research topics of 'Nonlinear system modeling via knot-optimizing B-spline networks'. Together they form a unique fingerprint.

Cite this