Nonlinear PLS modeling

Wold, Svante; Kettaneh-Wold, Nouna; Skagerberg, Bert

doi:10.1016/0169-7439(89)80111-x

Cited by 543 publications

(356 citation statements)

References 18 publications

Supporting

Mentioning

353

Contrasting

Unclassified

Order By: Relevance

“…In LPLS, the PLS output scores vector (u) is predicted using a linear function of the PLS input scores vector (t). QPLS is the quadratic partial least squares method introduced by Wold et al [4]. QPLS uses a quadratic function to model the PLS inner relation.…”

Section: Methodsmentioning

confidence: 99%

“…Several nonlinear versions of the function methods have been developed such as quadratic PLS, spline PLS, and neural networks PLS. Wold et al [4] have pioneered the quadratic PLS techniques and many other methods are variants of their work. They extended the two blocks linear PLS model using a quadratic function instead of a linear function.…”

Section: Introductionmentioning

confidence: 99%

“…Each added block of columns is a transformed data of the original matrix using a certain nonlinear function such as logarithms, squared values, cross products, binary transformation, to name a few [4][5][6], i.e.,X ¼ ½X; f 1 ðXÞ; . .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A nonlinear partial least squares algorithm using quadratic fuzzy inference system

Abdel-Rahman

Lim

2009

Journal of Chemometrics

View full text Add to dashboard Cite

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).

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A nonlinear partial least squares algorithm using quadratic fuzzy inference system

Abdel-Rahman

Lim

2009

Journal of Chemometrics

View full text Add to dashboard Cite

show abstract

“…In fact, in principle such tools are not able to directly describe the underlying structure of datasets affected by severe non-linearities, since they a priori assume this structure to be linear [7]. In the last decades, many novel approaches have been proposed to handle similar situations like non-linear PLS [8][9][10][11][12][13][14][15] or artificial neural networks [16]. Nevertheless, these strategies often require the optimisation of many adjustable parameters and may show overfitting and local minima.…”

Section: Introductionmentioning

confidence: 99%

Two-stage least squares and indirect least squares algorithms for simultaneous equations models

López-Espín

Vidal

Giménez

2012

Journal of Computational and Applied Mathematics

View full text Add to dashboard Cite

“…Over the years, numerous extensions have been proposed to make PLS applicable for nonlinear processes. Wold et al [39] introduced quadratic PLS (QPLS), in which nonlinear (quadratic) functions are used to describe the inner relationships between the input score vectors and the latent variables. Other nonlinear versions of PLS (NLPLS) based on artificial neural networks (ANNs) have been proposed by Qin and McAvoy [28] and Malthouse et al [18].…”

Section: Introductionmentioning

confidence: 99%

Fault tolerant control for a dearomatisation process

Sourander¹,

Vermasvuori²,

Sauter³

et al. 2009

Journal of Process Control

View full text Add to dashboard Cite

Author 's accepted manuscript, published in Journal of Process Control 19 (2009) [1091][1092][1093][1094][1095][1096][1097][1098][1099][1100][1101][1102] Fault tolerant control for a dearomatisation process AbstractIn this paper, a fault tolerant control (FTC) for a dearomatisation process in the presence of faults in online product quality analysers is presented. The FTC consists of a fault detection system (FDI) and a logic for triggering predefined FTC actions. FDI is achieved by combining several process data driven approaches for detecting faults in online quality analysers. The FTC exploits the diagnostic information in adapting a quality controller (MPC) to the faulty situation by manipulating tuning parameters of the MPC to produce both proactive and reactive strategies. The proposed FTC was implemented, tested offline and validated onsite at the Naantali oil refinery. The successful testing and plant validation results are presented and discussed.

show abstract

Nonlinear PLS modeling

Cited by 543 publications

References 18 publications

A nonlinear partial least squares algorithm using quadratic fuzzy inference system

A nonlinear partial least squares algorithm using quadratic fuzzy inference system

Two-stage least squares and indirect least squares algorithms for simultaneous equations models

Fault tolerant control for a dearomatisation process

Contact Info

Product

Resources

About