Reduced state nonlinear prediction with state reduction

B. Mulgrew; K. Nisbet; S. McLaughlin

Reduced state nonlinear prediction with state reduction

B. Mulgrew, K. Nisbet, S. McLaughlin

Research output: Contribution to conference › Paper

Abstract

The signal subspace technique for state reduction in nonlinear Volterra series (VS) and radial basis function (RBF) predictors are examined. The concept of applying signal subspace techniques to nonlinear prediction problems was first presented by Mulgrew et al. (see IEE Colloquium on Adaptive Filters, 1991). Since then, two alternative approaches (the indirect method and the direct method) have been developed. Results are presented which demonstrate the effectiveness of these techniques when applied to the prediction of chaotic time series

Original language	English
Publication status	Published - Feb 1993
Externally published	Yes
Event	IEE Colloquium New Directions in Adaptive Signal Processing - London Duration: 16 Feb 1993 → 16 Feb 1993

Conference

Conference	IEE Colloquium New Directions in Adaptive Signal Processing
City	London
Period	16/02/93 → 16/02/93

Keywords

adaptive nonlinear prediction ,
state reduction
signal subspace technique
nonlinear Volterra series
radial basis function
chaotic time series

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@conference{3d258ac8bb1c4601a98479aa64346347,

title = "Reduced state nonlinear prediction with state reduction",

abstract = "The signal subspace technique for state reduction in nonlinear Volterra series (VS) and radial basis function (RBF) predictors are examined. The concept of applying signal subspace techniques to nonlinear prediction problems was first presented by Mulgrew et al. (see IEE Colloquium on Adaptive Filters, 1991). Since then, two alternative approaches (the indirect method and the direct method) have been developed. Results are presented which demonstrate the effectiveness of these techniques when applied to the prediction of chaotic time series",

keywords = "adaptive nonlinear prediction ,, state reduction, signal subspace technique, nonlinear Volterra series, radial basis function, chaotic time series",

author = "B. Mulgrew and K. Nisbet and S. McLaughlin",

year = "1993",

month = feb,

language = "English",

note = "IEE Colloquium New Directions in Adaptive Signal Processing ; Conference date: 16-02-1993 Through 16-02-1993",

}

TY - CONF

T1 - Reduced state nonlinear prediction with state reduction

AU - Mulgrew, B.

AU - Nisbet, K.

AU - McLaughlin, S.

PY - 1993/2

Y1 - 1993/2

N2 - The signal subspace technique for state reduction in nonlinear Volterra series (VS) and radial basis function (RBF) predictors are examined. The concept of applying signal subspace techniques to nonlinear prediction problems was first presented by Mulgrew et al. (see IEE Colloquium on Adaptive Filters, 1991). Since then, two alternative approaches (the indirect method and the direct method) have been developed. Results are presented which demonstrate the effectiveness of these techniques when applied to the prediction of chaotic time series

AB - The signal subspace technique for state reduction in nonlinear Volterra series (VS) and radial basis function (RBF) predictors are examined. The concept of applying signal subspace techniques to nonlinear prediction problems was first presented by Mulgrew et al. (see IEE Colloquium on Adaptive Filters, 1991). Since then, two alternative approaches (the indirect method and the direct method) have been developed. Results are presented which demonstrate the effectiveness of these techniques when applied to the prediction of chaotic time series

KW - adaptive nonlinear prediction ,

KW - state reduction

KW - signal subspace technique

KW - nonlinear Volterra series

KW - radial basis function

KW - chaotic time series

M3 - Paper

T2 - IEE Colloquium New Directions in Adaptive Signal Processing

Y2 - 16 February 1993 through 16 February 1993

ER -

Reduced state nonlinear prediction with state reduction

Abstract

Conference

Keywords

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