Nonlinear PLS modelling using radial basis functions

D. J.H. Wilson; G. W. Irwin; G. Lightbody

doi:10.1109/acc.1997.612069

Nonlinear PLS modelling using radial basis functions

D. J.H. Wilson
, G. W. Irwin
, G. Lightbody

Queen's University Belfast

Research output: Contribution to journal › Article › peer-review

Abstract

A new approach to nonlinear Partial Least Squares (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a novel technique (the hybrid BFGS algorithm) for training the networks. Results are given to show the performance with a number of different simulation examples, including a model of an industrial overheads condenser and reflux drum plant. Results confirm a significant improvement over linear PLS.

Original language	English
Pages (from-to)	3275-3276
Number of pages	2
Journal	Proceedings of the American Control Conference
Volume	5
DOIs	https://doi.org/10.1109/acc.1997.612069
Publication status	Published - 1997
Externally published	Yes
Event	Proceedings of the 1997 American Control Conference. Part 3 (of 6) - Albuquerque, NM, USA Duration: 4 Jun 1997 → 6 Jun 1997

Access to Document

10.1109/acc.1997.612069

Cite this

@article{c6781f585950422fba0703cc27d772eb,

title = "Nonlinear PLS modelling using radial basis functions",

abstract = "A new approach to nonlinear Partial Least Squares (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a novel technique (the hybrid BFGS algorithm) for training the networks. Results are given to show the performance with a number of different simulation examples, including a model of an industrial overheads condenser and reflux drum plant. Results confirm a significant improvement over linear PLS.",

author = "Wilson, \{D. J.H.\} and Irwin, \{G. W.\} and G. Lightbody",

year = "1997",

doi = "10.1109/acc.1997.612069",

language = "English",

volume = "5",

pages = "3275--3276",

journal = "Proceedings of the American Control Conference",

issn = "0743-1619",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings of the 1997 American Control Conference. Part 3 (of 6) ; Conference date: 04-06-1997 Through 06-06-1997",

}

TY - JOUR

T1 - Nonlinear PLS modelling using radial basis functions

AU - Wilson, D. J.H.

AU - Irwin, G. W.

AU - Lightbody, G.

PY - 1997

Y1 - 1997

N2 - A new approach to nonlinear Partial Least Squares (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a novel technique (the hybrid BFGS algorithm) for training the networks. Results are given to show the performance with a number of different simulation examples, including a model of an industrial overheads condenser and reflux drum plant. Results confirm a significant improvement over linear PLS.

AB - A new approach to nonlinear Partial Least Squares (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a novel technique (the hybrid BFGS algorithm) for training the networks. Results are given to show the performance with a number of different simulation examples, including a model of an industrial overheads condenser and reflux drum plant. Results confirm a significant improvement over linear PLS.

UR - https://www.scopus.com/pages/publications/0030718645

U2 - 10.1109/acc.1997.612069

DO - 10.1109/acc.1997.612069

M3 - Article

AN - SCOPUS:0030718645

SN - 0743-1619

VL - 5

SP - 3275

EP - 3276

JO - Proceedings of the American Control Conference

JF - Proceedings of the American Control Conference

T2 - Proceedings of the 1997 American Control Conference. Part 3 (of 6)

Y2 - 4 June 1997 through 6 June 1997

ER -

Nonlinear PLS modelling using radial basis functions

Abstract

Access to Document

Other files and links

Fingerprint

Cite this