Algorithm for finding an interpretable simple neural network solution using PLS
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Algorithm for finding an interpretable simple neural network solution using PLS. / Bro, Rasmus.
In: Journal of Chemometrics, Vol. 9, No. 5, 01.01.1995, p. 423-430.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Algorithm for finding an interpretable simple neural network solution using PLS
AU - Bro, Rasmus
PY - 1995/1/1
Y1 - 1995/1/1
N2 - This communication describes the combination of a feedforward neural network (NN) with one hidden neuron and partial least squares (PLS) regression. Through training of the neural network with an algorithm that is a combination of a modified simplex, PLS and certain numerical restrictions, one gains an NN solution that has several feasible properties: (i) as in PLS the solution is qualitatively interpretable; (ii) it works faster than or comparably with ordinary training algorithms for neural networks; (iii) it contains the linear solution as a limiting case. Another very important aspect of this training algorithm is the fact that outlier detection as in ordinary PLS is possible through loadings, scores and residuals. The algorithm is used on a simple non‐linear problem concerning fluorescence spectra of white sugar solutions.
AB - This communication describes the combination of a feedforward neural network (NN) with one hidden neuron and partial least squares (PLS) regression. Through training of the neural network with an algorithm that is a combination of a modified simplex, PLS and certain numerical restrictions, one gains an NN solution that has several feasible properties: (i) as in PLS the solution is qualitatively interpretable; (ii) it works faster than or comparably with ordinary training algorithms for neural networks; (iii) it contains the linear solution as a limiting case. Another very important aspect of this training algorithm is the fact that outlier detection as in ordinary PLS is possible through loadings, scores and residuals. The algorithm is used on a simple non‐linear problem concerning fluorescence spectra of white sugar solutions.
KW - interpretable
KW - neural network
KW - PLS
KW - training
UR - http://www.scopus.com/inward/record.url?scp=84984373569&partnerID=8YFLogxK
U2 - 10.1002/cem.1180090508
DO - 10.1002/cem.1180090508
M3 - Journal article
AN - SCOPUS:84984373569
VL - 9
SP - 423
EP - 430
JO - Journal of Chemometrics
JF - Journal of Chemometrics
SN - 0886-9383
IS - 5
ER -
ID: 222926318