Parameter Estimation of Hammerstein Model Based on a Gradient Algorithm in Wavelet Domain

被引：0

作者：

Li Zhen-Qiang ^{[1
]}

Zou Li-Rong ^{[1
]}

Huang Jie ^{[1
]}

机构：

[1] Guangxi Univ Sci & Technol, Inst Elect & Informat Engn, Liuzhou 545006, Peoples R China

来源：

PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016 | 2016年

关键词：

Wavelet Transform; Hammerstein Model; Gradient Method; Parameter Estimation; IDENTIFICATION; SYSTEMS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For the discrete nonlinear Hammerstein model with the noise corrupted output data, a gradient method is proposed to estimate the parameters of the model with the input-output data in wavelet domain directly. With the wavelet theory being developed it plays an important role in signal processing. By means of wavelet transform, the signal has both characteristics of time and frequency and becomes a signal in wavelet domain, and increasing the ratio of signal to noise. The parameters of model are estimated by the wavelet gradient method, compare with the gradient method in time domain, the proposed method is effective by the simulation.

引用

页码：2081 / 2086

页数：6

共 12 条

[1]

[Anonymous], 1992, CBMS-NSF Reg. Conf. Ser. in Appl. Math

[2]

[Anonymous], 1999, SYSTEM IDENTIFICATIO

[3] Frequency domain identification of Hammerstein models [J].

Bai, EW .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2003, 48 (04) :530-542

[4] Strong consistency of recursive identification for Hammerstein systems with discontinuous piecewise-linear memoryless block [J].

Chen, HF .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2005, 50 (10) :1612-1617

[5] ORTHONORMAL BASES OF COMPACTLY SUPPORTED WAVELETS [J].

DAUBECHIES, I .

COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 1988, 41 (07) :909-996

[6] Identification of Hammerstein nonlinear ARMAX systems [J].

Ding, F ;

Chen, TW .

AUTOMATICA, 2005, 41 (09) :1479-1489

[7]

Ding F, 2006, NONLINEAR DYNAM, V45, P31, DOI 10.1007/s11071-006-1850-7

[8] Subspace identification of Hammerstein systems using least squares support vector machines [J].

Goethals, I ;

Pelckmans, K ;

Suykens, JAK ;

De Moor, B .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2005, 50 (10) :1509-1519

[9] Neural network approach for identification of Hammerstein systems [J].

Janczak, A .

INTERNATIONAL JOURNAL OF CONTROL, 2003, 76 (17) :1749-1766

[10]

Narendra K., 1966, IEEE T AUTOMAT CONTR, P546, DOI DOI 10.1109/TAC.1966.1098387

← 1 2 →