Open AccessComparative Analysis of Pitching Prediction Algorithms
Author(s) -
Д. Антонов,
О. В. Зайцев,
Yu. А. Litvinenko
Publication year - 2022
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1215/1/012002
Subject(s) - algorithm , sensitivity (control systems) , kalman filter , computer science , artificial neural network , process (computing) , markov model , markov chain , artificial intelligence , machine learning , engineering , electronic engineering , operating system
Two algorithms are described in the paper; one of them is the Kalman filter, which is based on the use of a pitching mathematical model, and the second uses a neural network in which the model is considered unknown. The results of the algorithms sensitivity analysis to the parameters of the model and its influence on the potential accuracy of prediction are presented. A stationary narrow-band second-order Markov process is used as a model of the ship pitching, which was used to form the input signal of the algorithms. Also, the results of the algorithms simulation in predicting real data are presented.