[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Wang et al., 2018 - Google Patents

A SVM-based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion

Wang et al., 2018

View PDF
Document ID
2522110762984741255
Author
Wang C
Cheng X
Chen S
Li G
Zhang H
Publication year
Publication venue
2018 IEEE International Conference on Mechatronics and Automation (ICMA)

External Links

Snippet

This paper presents a novel method that combines support vector machine (SVM) with sensitivity analysis (SA) to analyze sensor data for ship motion modeling. In order to investigate how each model input contributes to the model's output, the PAWN method …
Continue reading at ntnuopen.ntnu.no (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Nielsen A concise account of techniques available for shipboard sea state estimation
Cheng et al. Data-driven uncertainty and sensitivity analysis for ship motion modeling in offshore operations
Ren et al. Sea state estimation based on vessel motion responses: Improved smoothness and robustness using Bézier surface and L1 optimization
Han et al. Vessel hydrodynamic model tuning by discrete Bayesian updating using simulated onboard sensor data
Cheng et al. A neural-network-based sensitivity analysis approach for data-driven modeling of ship motion
Dalheim et al. Uncertainty in the real-time estimation of ship speed through water
Chen et al. Grey-box identification modeling of ship maneuvering motion based on LS-SVM
CN117198330B (en) Sound source identification method and system and electronic equipment
Xue et al. Online identification of a ship maneuvering model using a fast noisy input Gaussian process
CN103605886A (en) Multi-model self-adaptive fusion filtering method of ship dynamic positioning system
Ngo et al. An analysis of regression models for predicting the speed of a wave glider autonomous surface vehicle
Cheng et al. Simplifying neural network based model for ship motion prediction: a comparative study of sensitivity analysis
Wang et al. noiseNet: A neural network to predict marine propellers’ underwater radiated noise
Nielsen Response-based estimation of sea state parameters—influence of filtering
Mounet et al. Deriving spatial wave data from a network of buoys and ships
CN115859116A (en) Marine environment field reconstruction method based on radial basis function regression interpolation method
Wang et al. A SVM-based Sensitivity Analysis Approach for Data-Driven Modeling of Ship Motion
CN107064893A (en) Pareto distribution with wide scope method for parameter estimation based on logarithmic moment
Ryu et al. Incremental Low-Rank Dynamic Mode Decomposition Model for Efficient Forecast Dissemination and Onboard Forecasting
He et al. Fault diagnosis of full-drive AUV based on graph theory and recurrent neural network
Shen et al. Ship dynamics model identification based on Semblance least square support vector machine
CN117037450A (en) Environment early warning method, device, equipment and medium of intelligent piling ship
Chen et al. Experimental demonstration of acoustic inversion using an AUV carrying source
Song et al. Uncertainty-Aware Acoustic Localization and Mapping for Underwater Robots
Peart et al. Multi-fidelity surrogate models for VPP aerodynamic input data