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Parameter Prediction of the Non-Linear Nomoto Model for Different Ship Loading Conditions Using Support Vector Regression

https://doi.org/10.3390/jmse11050903
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00773-014-0293-yopenalex-snapshot
Introduction of MMG standard method for ship maneuvering predictions
inferredcites →https://doi.org/10.1007/s00773-019-00639-yopenalex-snapshot
Parameter identification of ship motion model based on multi-innovation methods
inferredcites →https://doi.org/10.1016/j.oceaneng.2012.05.001openalex-snapshot
Estimating maneuvering coefficients using system identification methods with experimental, system-based, and CFD free-running trial data
inferredcites →https://doi.org/10.1016/j.oceaneng.2019.106612openalex-snapshot
Hydrodynamic parameter identification for ship manoeuvring mathematical models using a Bayesian approach
inferredcites →https://doi.org/10.1016/j.oceaneng.2021.109605openalex-snapshot
A method for the direct assessment of ship collision damage and flooding risk in real conditions
inferredcites →https://doi.org/10.1016/j.oceaneng.2021.109964openalex-snapshot
Effect of the loading conditions on the maneuverability of a container ship
inferredcites →https://doi.org/10.1016/j.oceaneng.2021.110471openalex-snapshot
Real-time parameter identification of ship maneuvering response model based on nonlinear Gaussian Filter
inferredcites →https://doi.org/10.1016/j.ress.2022.108697openalex-snapshot
A machine learning method for the evaluation of ship grounding risk in real operational conditions
inferredcites →https://doi.org/10.1016/s0893-6080(03)00169-2openalex-snapshot
Practical selection of SVM parameters and noise estimation for SVM regression
inferredcites →https://doi.org/10.1080/17445302.2021.1927600openalex-snapshot
Parameter identification of ship manoeuvring model under disturbance using support vector machine method
inferredcites →https://doi.org/10.1109/48.838986openalex-snapshot
Modeling and identification of open-frame variable configuration unmanned underwater vehicles
inferredcites →https://doi.org/10.1115/1.4032892openalex-snapshot
Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization
inferredcites →https://doi.org/10.1115/omae2020-18069openalex-snapshot
Black-Box Modeling of Ship Maneuvering Motion Using System Identification Method Based on BP Neural Network
inferredcites →https://doi.org/10.1155/2017/2561383openalex-snapshot
Inference Engine in an Intelligent Ship Course-Keeping System
inferredcites →https://doi.org/10.1177/14750902231157852openalex-snapshot
A deep learning method for the prediction of 6-DoF ship motions in real conditions
inferredcites →https://doi.org/10.1177/1729881420921016openalex-snapshot
The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification algorithm
inferredcites →https://doi.org/10.12716/1001.11.01.01openalex-snapshot
Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines
inferredcites →https://doi.org/10.25043/19098642.160openalex-snapshot
Obtaining First and Second Order Nomoto Models of a Fluvial Support Patrol using Identification Techniques
inferredcites →https://doi.org/10.2534/jjasnaoe1952.1956.99_75openalex-snapshot
On the Steering Qualities of Ships
inferredcites →https://doi.org/10.3233/isp-1978-2529202openalex-snapshot
On the mathematical model of manoeuvring motion of ships
inferredcites →https://doi.org/10.3390/jmse10050581openalex-snapshot
A Novel Parameter Identification Algorithm for 3-DOF Ship Maneuvering Modelling Using Nonlinear Multi-Innovation
inferredcites →https://doi.org/10.3940/rina.ijme.a3.2018.448openalex-snapshot
Estimation of Hydrodynamic Coefficients of a Nonlinear Manoeuvring Mathematical Model With Free-Running Ship Model Tests
inferredcites →https://doi.org/10.51400/2709-6998.2525openalex-snapshot
Fundamental Properties of Linear Ship Steering Dynamic Models

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Numerical Modeling and Testing of Ship Maneuvering and Hydrodynamics for Inland Unconventional Ship
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Optimal Path Following Controller Design Based on Linear Quadratic Regulator for Underactuated Ships in Varying Wave and Wind Conditions
inferred← citeshttps://doi.org/10.1016/j.dsp.2024.104445openalex-snapshot
Parameter identification algorithm for ship manoeuvrability and wave peak model based multi-innovation stochastic gradient algorithm use data filtering technique
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111962openalex-snapshot
Online nonparametric identification modeling of ship maneuvering motion based on PSO-optimized incremental Gaussian mixture model
inferred← citeshttps://doi.org/10.1016/j.joes.2025.03.001openalex-snapshot
Variance-tuned ensemble Gaussian process regression for learning and prediction of ship maneuvering motion
inferred← citeshttps://doi.org/10.1016/j.oceaneng.2023.116595openalex-snapshot
A novel method for the evaluation of ship berthing risk using AIS data
inferred← citeshttps://doi.org/10.1016/j.oceaneng.2024.119582openalex-snapshot
Event-triggered predictive path following control of autonomous ships with an MMG model
inferred← citeshttps://doi.org/10.1016/j.oceaneng.2024.120064openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.oceaneng.2026.125842openalex-snapshot
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inferred← citeshttps://doi.org/10.1017/s0373463326101532openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s41598-026-41980-8openalex-snapshot
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Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes
inferred← citeshttps://doi.org/10.1109/icimia67127.2025.11200543openalex-snapshot
Intelligent Maritime Collision Avoidance Through Integrated Decision and Control Algorithms based on Vessel Dynamics
inferred← citeshttps://doi.org/10.1109/idap64064.2024.10710751openalex-snapshot
A Study Evaluating the Performance of Nonlinear Nomoto Models in Predicting the Manoeuvring of Car Carrier
inferred← citeshttps://doi.org/10.1109/tase.2025.3579396openalex-snapshot
Two-Step Event-Triggered Data Driven Model Predictive Control for Trajectory Tracking of Unmanned Surface Vessel Under Environmental Disturbances
inferred← citeshttps://doi.org/10.1177/14750902261443976openalex-snapshot
Active disturbance rejection-based robust course control for ships with uncertain steering dynamics
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1014315openalex-snapshot
Integrated computational and experimental analysis explores FOLH1 expression patterns across cancers and nominates melatonin as a potential modulator in prostate cancer models
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Efficient data-driven machine learning models for scour depth predictions at sloping sea defences
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Ship Model Identification Using Interpretable 4-DOF Maneuverability Models for River Combat Boat
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Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction
inferred← citeshttps://doi.org/10.3390/jmse14131207openalex-snapshot
Online Nonparametric Identification Modeling of Intelligent Ship Maneuvering Dynamics Based on Expectation- Maximization Algorithm
inferred← citeshttps://doi.org/10.3390/s26103199openalex-snapshot
A System Identification Approach to Motion Model Based on Full-Scale Ship Maneuvering Data
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