Partial least squares (PLS) regression is used for identifying the hydrodynamic derivatives in the Abkowitz model for ship maneuvering motion. To identify the dynamic characteristics in ship maneuvering motion, the derivatives of hydrodynamic model's outputs are set as the target output of the PLS identification model. To verify the effectiveness of PLS parametric identification method in processing data with high dimensionality and heavy multicollinearity, the identified results of the hydrodynamic derivatives from the simulated 20 deg/20 deg zigzag test are compared with the planar motion mechanism (PMM) test results. The performance of PLS regression is also compared with that of the conventional least squares (LS) regression using the same dataset. Simulation results show the satisfactory identification and generalization performances of PLS regression and its superiority in comparison with the LS method, which demonstrates its capability in processing measurement data with high dimensionality and heavy multicollinearity, especially in processing data with small sample size.

References

References
1.
Fossen
,
T. I.
,
1994
,
Guidance and Control of Ocean Vehicles, John
Wiley
,
New York
.
2.
Vahabi
,
M.
,
Mehdizadeh
,
E.
,
Kabganian
,
M.
, and
Barazandeh
,
F.
,
2011
, “
Experimental Identification of IPMC Actuator Parameters Through Incorporation of Linear and Nonlinear Least Squares Methods
,”
Sens. Actuators, A
,
168
(
1
), pp.
140
148
.10.1016/j.sna.2011.03.034
3.
Tanaka
,
M.
,
Matsumoto
,
T.
, and
Yamamura
,
H.
,
2004
, “
Application of BEM With Extended Kalman Filter to Parameter Identification of an Elastic Plate Under Dynamic Loading
,”
Eng. Anal. Boundary Elem.
,
28
(
3
), pp.
213
219
.10.1016/S0955-7997(03)00052-3
4.
Xu
,
J.
, and
Quaddus
,
M.
,
2012
, “
Examining a Model of Knowledge Management Systems Adoption and Diffusion: A Partial Least Square Approach
,”
Knowl. -Based Syst.
,
27
, pp.
18
28
.10.1016/j.knosys.2011.10.003
5.
Boulesteix
,
A. L.
, and
Strimmer
,
K.
,
2007
, “
Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data
,”
Briefings Bioinf.
,
8
(1), pp.
32
44
.10.1093/bib/bbl016
6.
Ohsumi
,
A.
, and
Nakano
,
N.
,
2002
, “
Identification of Physical Parameters of a Flexible Structure From Noisy Measurement Data
,”
IEEE Trans. Instrum. Meas.
,
51
(
5
), pp.
923
929
.10.1109/TIM.2002.806023
7.
Geladi
,
P.
, and
Kowalski
,
B. R.
,
1986
, “
Partial Least-Squares Regression: A Tutorial
,”
Anal. Chim. Acta
,
185
, pp.
1
17
.10.1016/0003-2670(86)80028-9
8.
Kim
,
C. S.
, and
Kim
, I
. M.
,
2012
, “
Partial Parametric Estimation for Nonstationary Nonlinear Regressions
,”
J. Econometrics
,
167
(
2
), pp.
448
457
.10.1016/j.jeconom.2011.09.027
9.
Huang
,
S. C.
,
2011
, “
Forecasting Stock Indices With Wavelet Domain Kernel Partial Least Square Regressions
,”
Appl. Soft Comput.
,
11
(
8
), pp.
5433
5443
.10.1016/j.asoc.2011.05.015
10.
Lee
,
L.
,
Petter
,
S.
,
Fayard
,
D.
, and
Robinson
,
S.
,
2011
, “
On the Use of Partial Least Squares Path Modeling in Accounting Research
,”
Int. J. Account. Inf. Syst.
,
12
(
4
), pp.
305
328
.10.1016/j.accinf.2011.05.002
11.
von Stosch
,
M.
,
Oliveira
,
R.
,
Peres
,
J.
, and
de Azevedo
,
S. F.
,
2011
, “
A Novel Identification Method for Hybrid (N) PLS Dynamical Systems With Application to Bioprocesses
,”
Expert Syst. Appl.
,
38
(
9
), pp.
10862
10874
.10.1016/j.eswa.2011.02.117
12.
Yang
,
Z. J.
,
You
,
W. J.
, and
Ji
,
G. L.
,
2011
, “
Using Partial Least Squares and Support Vector Machines for Bankruptcy Prediction
,”
Expert Syst. Appl.
,
38
(
7
), pp.
8336
8342
.10.1016/j.eswa.2011.01.021
13.
Qu
,
H. N.
,
Li
,
G. Z.
, and
Xu
,
W. S.
,
2010
, “
An Asymmetric Classifier Based on Partial Least Squares
,”
Pattern Recognit.
,
43
(
10
), pp.
3448
3457
.10.1016/j.patcog.2010.05.002
14.
Xu
,
B.
,
He
,
J.
,
Rovekamp
,
R.
, and
Dyke
,
S. J.
,
2012
, “
Structural Parameters and Dynamic Loading Identification From Incomplete Measurements: Approach and Validation
,”
Mech. Syst. Signal Process.
,
28
, pp.
244
257
.10.1016/j.ymssp.2011.07.008
15.
Kourti
,
T.
, and
MacGregor
,
J. F.
,
1995
, “
Tutorial: Process Analysis, Monitoring and Diagnosis, Using Multivariate Regression Methods
,”
Chemom. Intell. Lab. Syst.
,
28
(
1
), pp.
3
21
.10.1016/0169-7439(95)80036-9
16.
Luo
,
W. L.
, and
Zou
,
Z. J.
,
2009
, “
Parametric Identification of Ship Manoeuvring Models by Using Support Vector Machines
,”
J. Ship Res.
,
53
(
1
), pp.
19
30
.
17.
Zhang
,
X. G.
, and
Zou
,
Z. J.
,
2011
, “
Identification of Abkowitz Model for Ship Manoeuvring Motion Using ε-Support Vector Regression
,”
J. Hydrodyn.
,
23
(
3
), pp.
353
360
.10.1016/S1001-6058(10)60123-0
18.
Chislett
,
M. S.
, and
Strøm-Tejsen
,
J.
,
1965
, “
Planar Motion Mechanism Tests and Full-Scale Steering and Maneuvering Predictions for a Mariner Class Vessel
,” Report No. Hy-6, Technical Report Hy-6, Hydro- and Aerodynamics Laboratory, Lyngby, Denmark.
19.
Castelán
,
M.
, and
Van Horebeek
,
J.
,
2009
, “
Relating Intensities With Three-Dimensional Facial Shape Using Partial Least Squares
,”
IET Comput. Vision
,
3
(
2
), pp.
60
73
.10.1049/iet-cvi.2008.0060
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