Algorithm for finding an interpretable simple neural network solution using PLS

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Algorithm for finding an interpretable simple neural network solution using PLS. / Bro, Rasmus.

I: Journal of Chemometrics, Bind 9, Nr. 5, 01.01.1995, s. 423-430.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Bro, R 1995, 'Algorithm for finding an interpretable simple neural network solution using PLS', Journal of Chemometrics, bind 9, nr. 5, s. 423-430. https://doi.org/10.1002/cem.1180090508

APA

Bro, R. (1995). Algorithm for finding an interpretable simple neural network solution using PLS. Journal of Chemometrics, 9(5), 423-430. https://doi.org/10.1002/cem.1180090508

Vancouver

Bro R. Algorithm for finding an interpretable simple neural network solution using PLS. Journal of Chemometrics. 1995 jan. 1;9(5):423-430. https://doi.org/10.1002/cem.1180090508

Author

Bro, Rasmus. / Algorithm for finding an interpretable simple neural network solution using PLS. I: Journal of Chemometrics. 1995 ; Bind 9, Nr. 5. s. 423-430.

Bibtex

@article{b2e0e817b2694cf39b78fe1be8351ba9,
title = "Algorithm for finding an interpretable simple neural network solution using PLS",
abstract = "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.",
keywords = "interpretable, neural network, PLS, training",
author = "Rasmus Bro",
year = "1995",
month = jan,
day = "1",
doi = "10.1002/cem.1180090508",
language = "English",
volume = "9",
pages = "423--430",
journal = "Journal of Chemometrics",
issn = "0886-9383",
publisher = "Wiley",
number = "5",

}

RIS

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