A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method

  • Delivopoulos E
  • Theocharis J
  • 3

    Readers

    Mendeley users who have this article in their library.
  • 14

    Citations

    Citations of this article.

Abstract

In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach. © 2004 Elsevier Inc. All rights reserved.

Author-supplied keywords

  • Classification
  • Group method of data handling
  • Optimal partial description modeling
  • Orthogonal least squares method
  • Polynomial neural networks
  • Time series modeling

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • E. Delivopoulos

  • J. B. Theocharis

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free