首页|University of Lisbon Researcher Details New Studies and Findings in the Area of Support Vector Machines (Effect of Sampling Rate in Sea Trial Tests on the Estim ation of Hydrodynamic Parameters for a Nonlinear Ship Manoeuvring Model)
University of Lisbon Researcher Details New Studies and Findings in the Area of Support Vector Machines (Effect of Sampling Rate in Sea Trial Tests on the Estim ation of Hydrodynamic Parameters for a Nonlinear Ship Manoeuvring Model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on have been pub lished. According to news reporting from Lisbon, Portugal, by NewsRx journalists, research stated, "This paper explores the impact of sampling rates during sea trials on the estimation of hydrodynamic parameters in a nonlinear manoeuvring m odel." Financial supporters for this research include Portuguese Foundation For Science And Technology; Fct. The news editors obtained a quote from the research from University of Lisbon: " Sea trials were carried out using an offshore patrol vessel and test data were c ollected. A nonlinear manoeuvring model is introduced to characterise the ship's manoeuvring motion, and the truncated least squares support vector machine is e mployed to estimate nondimensional hydrodynamic coefficients and their correspon ding uncertainties using the 25°-25° zigzag test. To assess the influence of the sampling rates, the training set is resampled offline with 14 sampling rates, r anging from 0.2 Hz to 5 Hz, encompassing a rate 10 times the highest frequency c omponent of the signal of interest. The results show that the higher sampling ra te can significantly diminish the parameter uncertainty." According to the news editors, the research concluded: "To obtain a robust estim ation of linear and nonlinear hydrodynamic coefficients, the sampling rate shoul d be higher than 10 times the highest frequency component of the signal of inter est, and 3-5 Hz is recommended for the case in this paper. The validation is als o carried out, which indicates that the proposed truncated least square support vector machine can provide a robust parameter estimation."
University of LisbonLisbonPortugalEuropeEmerging TechnologiesMachine LearningSupport Vector MachinesVector Machines
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