Sparse model identification using a forward orthogonal regression algorithm aided by mutual information

被引：55

作者：

Billings, Stephen A. ^{[1
]}

Wei, Hua-Liang ^{[1
]}

机构：

[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2007年 / 18卷 / 01期

关键词：

model selection; mutual information; orthogonal least squares (OLS); parameter estimation;

D O I：

10.1109/TNN.2006.886356

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

A sparse representation, with satisfactory approximation accuracy, is usually desirable in any nonlinear system identification and signal processing problem. A new forward orthogonal regression algorithm, with mutual information interference, is proposed for sparse model selection and parameter estimation. The new algorithm can be used to construct parsimonious linear-in-the-parameters models.

引用

页码：306 / 310

页数：5

共 21 条

[1]

NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].

AKAIKE, H .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) :716-723

[2]

[Anonymous], 2002, ADAPTIVE MODELLING E

[3]

USING MUTUAL INFORMATION FOR SELECTING FEATURES IN SUPERVISED NEURAL-NET LEARNING [J].

BATTITI, R .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (04) :537-550

[4]

A new class of wavelet networks for nonlinear system identification [J].

Billings, SA ;

Wei, HL .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2005, 16 (04) :862-874

[5]

IDENTIFICATION OF MIMO NON-LINEAR SYSTEMS USING A FORWARD-REGRESSION ORTHOGONAL ESTIMATOR [J].

BILLINGS, SA ;

CHEN, S ;

KORENBERG, MJ .

INTERNATIONAL JOURNAL OF CONTROL, 1989, 49 (06) :2157-2189

[6]

IDENTIFICATION OF NON-LINEAR OUTPUT-AFFINE SYSTEMS USING AN ORTHOGONAL LEAST-SQUARES ALGORITHM [J].

BILLINGS, SA ;

KORENBERG, MJ ;

CHEN, S .

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 1988, 19 (08) :1559-1568

[7]

Regularized orthogonal least squares algorithm for constructing radial basis function networks [J].

Chen, S ;

Chng, ES ;

Alkadhimi, K .

INTERNATIONAL JOURNAL OF CONTROL, 1996, 64 (05) :829-837

[8]

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design [J].

Chen, S ;

Hong, X ;

Harris, CJ .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2003, 48 (06) :1029-1036

[9]

CHEN S, 1992, INT J CONTROL, V56, P319, DOI [10.1080/00207179208934317, 10.1080/00207179008934126]

[10]

Cherkassky V, 2007, LEARNING DATA CONCEP

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