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

Delivopoulos, E. and Theocharis, J.B. (2004) A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method. Information Sciences, 168 (1-4). pp. 133-170. ISSN 0020-0255

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To link to this item DOI: 10.1016/j.ins.2004.02.001

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:33923
Uncontrolled Keywords:Group method of data handling; Polynomial neural networks; Orthogonal least squares method; Time series modeling; Optimal partial description modeling; Classification
Publisher:Elsevier

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