Parametric Identification of Abkowitz Model for Ship Maneuvering Motion by Using Partial Least Squares Regression

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Yin Jian-Chuan ◽  
Zou Zao-Jian ◽  
Xu Feng
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Parametric Identification ◽  
Small Sample ◽  
High Dimensionality ◽  
Pls Regression ◽  
Ship Maneuvering ◽  
Processing Data ◽  
Hydrodynamic Derivatives ◽  
Maneuvering Motion

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.

Determination of Iodine Value of Palm Oils Using Partial Least Squares Regression-Fourier Transform Infrared Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Nor Fazila Rasaruddin ◽  
Mas Ezatul Nadia Mohd Ruah ◽  
Mohamed Noor Hasan ◽  
Mohd Zuli Jaafar
Keyword(s):  
Fourier Transform ◽  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Correlation Coefficient ◽  
Iodine Value ◽  
Cross Validation ◽  
Pls Regression ◽  
Pure Sample

This paper shows the determination of iodine value (IV) of pure and frying palm oils using Partial Least Squares (PLS) regression with application of variable selection. A total of 28 samples consisting of pure and frying palm oils which acquired from markets. Seven of them were considered as high-priced palm oils while the remaining was low-priced. PLS regression models were developed for the determination of IV using Fourier Transform Infrared (FTIR) spectra data in absorbance mode in the range from 650 cm-1 to 4000 cm-1. Savitzky Golay derivative was applied before developing the prediction models. The models were constructed using wavelength selected in the FTIR region by adopting selectivity ratio (SR) plot and correlation coefficient to the IV parameter. Each model was validated through Root Mean Square Error Cross Validation, RMSECV and cross validation correlation coefficient, R2cv. The best model using SR plot was the model with mean centring for pure sample and model with a combination of row scaling and standardization of frying sample. The best model with the application of the correlation coefficient variable selection was the model with a combination of row scaling and standardization of pure sample and model with mean centering data pre-processing for frying sample. It is not necessary to row scaled the variables to develop the model since the effect of row scaling on model quality is insignificant.

Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Weilin Luo ◽  
C. Guedes Soares ◽  
Zaojian Zou
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Difference Method ◽  
Particle Swarm ◽  
Support Vector ◽  
Ship Maneuvering ◽  
Swarm Optimization ◽  
Hydrodynamic Derivatives ◽  
Vector Machines ◽  
Maneuvering Motion

Combined with the free-running model tests of KVLCC ship, the system identification (SI) based on support vector machines (SVM) is proposed for the prediction of ship maneuvering motion. The hydrodynamic derivatives in an Abkowitz model are determined by the Lagrangian factors and the support vectors in the SVM regression model. To obtain the optimized structural factors in SVM, particle swarm optimization (PSO) is incorporated into SVM. To diminish the drift of hydrodynamic derivatives after regression, a difference method is adopted to reconstruct the training samples before identification. The validity of the difference method is verified by correlation analysis. Based on the Abkowitz mathematical model, the simulation of ship maneuvering motion is conducted. Comparison between the predicted results and the test results demonstrates the validity of the proposed methods in this paper.

scholarly journals Evaluation of Chlorophyll-a Estimation Approaches Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression and Several Traditional Algorithms from Field Hyperspectral Measurements in the Seto Inland Sea, Japan

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2656 ◽  
Author(s):  
Zuomin Wang ◽  
Yuji Sakuno ◽  
Kazuhiko Koike ◽  
Shizuka Ohara
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Chlorophyll A ◽  
Predictive Ability ◽  
Seto Inland Sea ◽  
Band Model ◽  
Pls Regression ◽  
Chl A ◽  
The Seto Inland Sea ◽  
Two Band Model

Harmful algal blooms (HABs) occur frequently in the Seto Inland Sea, bringing significant economic and environmental losses for the area, which is well known as one of the world’s most productive fisheries. Our objective was to develop a quantitative model using in situ hyperspectral measurements in the Seto Inland Sea to estimate chlorophyll a (Chl-a) concentration, which is a significant parameter for detecting HABs. We obtained spectra and Chl-a data at six stations from 12 ship-based surveys between December 2015 and September 2017. In this study, we used an iterative stepwise elimination partial least squares (ISE-PLS) regression method along with several empirical and semi-analytical methods such as ocean chlorophyll, three-band model, and two-band model algorithms to retrieve Chl-a. Our results showed that ISE-PLS using both the water-leaving reflectance (RL) and the first derivative reflectance (FDR) had a better predictive ability with higher coefficient of determination (R2), lower root mean squared error (RMSE), and higher residual predictive deviation (RPD) values (R2 = 0.77, RMSE = 1.47 and RPD = 2.1 for RL; R2 = 0.78, RMSE = 1.45 and RPD = 2.13 for FDR). However, in this study the ocean chlorophyll (OC) algorithms had poor predictive ability and the three-band and two-band model algorithms did not perform well in areas with lower Chl-a concentrations. These results support ISE-PLS as a potential coastal water quality assessment method using hyperspectral measurements.

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