Nonlinear PLS using Radial Basis Functions

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

doi:10.1177/014233129701900406

Nonlinear PLS 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 Projection to Latent Structures (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a hybrid optimisation technique for training the networks. Results are given showing an improvement in modelling performance over linear PLS for a variety of problems. An application of the technique to fault detection on a validated model of an industrial distillation plant is also demonstrated.

Original language	English
Pages (from-to)	211-220
Number of pages	10
Journal	Transactions of the Institute of Measurement and Control
Volume	19
Issue number	4
DOIs	https://doi.org/10.1177/014233129701900406
Publication status	Published - Oct 1997
Externally published	Yes

Keywords

fault detection
Nonlinear partial least squares
radial basis functions
statistical modelling

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10.1177/014233129701900406

Cite this

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abstract = "A new approach to nonlinear Projection to Latent Structures (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a hybrid optimisation technique for training the networks. Results are given showing an improvement in modelling performance over linear PLS for a variety of problems. An application of the technique to fault detection on a validated model of an industrial distillation plant is also demonstrated.",

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AU - Lightbody, G.

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N2 - A new approach to nonlinear Projection to Latent Structures (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a hybrid optimisation technique for training the networks. Results are given showing an improvement in modelling performance over linear PLS for a variety of problems. An application of the technique to fault detection on a validated model of an industrial distillation plant is also demonstrated.

AB - A new approach to nonlinear Projection to Latent Structures (PLS) modelling using Radial Basis Function (RBF) neural networks to provide a nonlinear inner relationship is described, along with a hybrid optimisation technique for training the networks. Results are given showing an improvement in modelling performance over linear PLS for a variety of problems. An application of the technique to fault detection on a validated model of an industrial distillation plant is also demonstrated.

KW - fault detection

KW - Nonlinear partial least squares

KW - radial basis functions

KW - statistical modelling

UR - https://www.scopus.com/pages/publications/0031327201

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JO - Transactions of the Institute of Measurement and Control

JF - Transactions of the Institute of Measurement and Control

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ER -

Nonlinear PLS using Radial Basis Functions

Abstract

Keywords

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