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Proceedings Paper

Inverse model formulation of partial least-squares regression: a robust neural network approach
Author(s): Fredric M. Ham; Thomas M. McDowall
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Paper Abstract

The Partial Least-Squares Regression (PLSR) approach to statistical calibration model development has been formulated using an inverse model. The inverse model PLSR algorithm is implemented using the Partial Least Squares neural NETwork (PLSNET) architecture. Generalized neural network learning rules derived from a statistical representation error criterion are presented. These learning rules will accommodate a quadratic optimization criterion, providing the linear solution. Optimization functions which grow less than quadratically can also be used to provide a robust solution when the empirical data contains impulsive and colored noise and outliers. The robust optimization criterion also accounts for the higher-order statistics associated with the input data. The inverse model PLSNET learning rules require fewer mathematical operations per weight update than the forward model robust PLSNET algorithms, resulting in faster convergence in many cases.

Paper Details

Date Published: 25 March 1998
PDF: 12 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304821
Show Author Affiliations
Fredric M. Ham, Florida Institute of Technology (United States)
Thomas M. McDowall, Florida Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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