Vector subspaces in nonlinear prediction

Kenneth Nisbet; Steven McLaughlin; Bernard Mulgrew

Vector subspaces in nonlinear prediction

Kenneth Nisbet, Steven McLaughlin, Bernard Mulgrew

School of Health and Life Sciences

Research output: Contribution to conference › Paper

Abstract

Radial basis function and Volterra series predictors are examined with a view to reducing their complexity while maintaining prediction performance. A geometrical interpretation of the problem is presented. This interpretation indicates that while a multiplicity of choices of reduced state predictor exist, some may be better than others in terms of the numerical conditioning of the solution.

Original language	English
Publication status	Published - 22 Nov 1991
Event	IEE Colloquium on Adaptive Filtering, Non-Linear Dynamics and Neural Networks - London, United Kingdom Duration: 22 Nov 1991 → 22 Nov 1991

Conference

Conference	IEE Colloquium on Adaptive Filtering, Non-Linear Dynamics and Neural Networks
Country/Territory	United Kingdom
City	London
Period	22/11/91 → 22/11/91

Access to Document

https://ieeexplore.ieee.org/document/263743

Cite this

@conference{da07d8c1dae34a48b082cfee183c6338,

title = "Vector subspaces in nonlinear prediction",

abstract = "Radial basis function and Volterra series predictors are examined with a view to reducing their complexity while maintaining prediction performance. A geometrical interpretation of the problem is presented. This interpretation indicates that while a multiplicity of choices of reduced state predictor exist, some may be better than others in terms of the numerical conditioning of the solution.",

author = "Kenneth Nisbet and Steven McLaughlin and Bernard Mulgrew",

year = "1991",

month = nov,

day = "22",

language = "English",

note = " IEE Colloquium on Adaptive Filtering, Non-Linear Dynamics and Neural Networks ; Conference date: 22-11-1991 Through 22-11-1991",

}

TY - CONF

T1 - Vector subspaces in nonlinear prediction

AU - Nisbet, Kenneth

AU - McLaughlin, Steven

AU - Mulgrew, Bernard

PY - 1991/11/22

Y1 - 1991/11/22

N2 - Radial basis function and Volterra series predictors are examined with a view to reducing their complexity while maintaining prediction performance. A geometrical interpretation of the problem is presented. This interpretation indicates that while a multiplicity of choices of reduced state predictor exist, some may be better than others in terms of the numerical conditioning of the solution.

AB - Radial basis function and Volterra series predictors are examined with a view to reducing their complexity while maintaining prediction performance. A geometrical interpretation of the problem is presented. This interpretation indicates that while a multiplicity of choices of reduced state predictor exist, some may be better than others in terms of the numerical conditioning of the solution.

M3 - Paper

T2 - IEE Colloquium on Adaptive Filtering, Non-Linear Dynamics and Neural Networks

Y2 - 22 November 1991 through 22 November 1991

ER -

Vector subspaces in nonlinear prediction

Abstract

Conference

Access to Document

Fingerprint

Cite this