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A nonlinear partial least squares algorithm using quadratic fuzzy inference system
Author(s) -
AbdelRahman Araby I.,
Lim Gino J.
Publication year - 2009
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1249
Subject(s) - partial least squares regression , mathematics , fuzzy logic , quadratic equation , variable elimination , mean squared error , latent variable , statistics , nonlinear system , algorithm , inference , artificial intelligence , computer science , physics , quantum mechanics , geometry
We introduce a new nonlinear partial least squares algorithm ‘Quadratic Fuzzy PLS (QFPLS)’ that combines the outer linear Partial Least Squares (PLS) framework and the Takagi–Sugeno–Kang (TSK) fuzzy inference system. The inner relation between the input and the output PLS score vectors is modeled by a quadratic TSK fuzzy inference system. The performance of the proposed QFPLS method is tested and compared against four other well‐known partial least squares methods (Linear PLS (LPLS), Quadratic PLS (QPLS), Linear Fuzzy PLS (LFPLS), and Neural Network PLS (NNPLS)) on various different types of randomly generated test data. QFPLS outperformed competitors based on two comparison measures: the output variables cumulative per cent variance captured by the PLS latent variables and the root mean‐square error of prediction (RMSEP). Copyright © 2009 John Wiley & Sons, Ltd.