Inverse model formulation of partial least-squares regression: a robust neural network approach

Fredric M. Ham; Thomas M. McDowall

doi:10.1117/12.304821

25 March 1998 Inverse model formulation of partial least-squares regression: a robust neural network approach

Fredric M. Ham, Thomas M. McDowall

Proceedings Volume 3390, Applications and Science of Computational Intelligence; (1998) https://doi.org/10.1117/12.304821
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

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Fredric M. Ham and Thomas M. McDowall "Inverse model formulation of partial least-squares regression: a robust neural network approach", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); https://doi.org/10.1117/12.304821

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