Multi-dimensional Local Weighted Regression Ship Motion Identification Modeling Based on Particle Swarm optimization

This paper explores a fast and efficient method for identifying and modeling ship maneuvering motion, and conducts a comprehensive experiment. Through the ship maneuvering test, the dynamics interaction between ship and the environment is obtained. Then, the LWL (Locally Weighted Learning algorithm) underlying architecture is constructed by sparse Gaussian Process to reduce the data requirements of LWL-based ship maneuvering motion modeling and to improve the performance for LWL. On this basis, a non-parametric model of ship maneuvering motion is established based on the locally weighted sparse Gaussian Process, and the traditional mathematical model of ship maneuvering motion is replaced by the generative model. This generative model considers the hydrodynamic effects of ships, and reduces the sensitivity of local weighted learning to sample data. In addition, matrix operations are transferred to the auxiliary platform to optimize the calculation performance of the method. Finally, the simulation results of ship maneuvering motion indicate that this method has the characteristics of efficiency, rapidity and universality, and its accuracy conforms to engineering practice.

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Multi-dimensional Local Weighted Regression Ship Motion Identification Modeling Based on Particle Swarm optimization

Cited by 2 publications

References 11 publications

Support Vector Regression Ship Motion Identification Modeling Based on Grey Wolf Optimizer

Support Vector Regression Ship Motion Identification Modeling Based on Grey Wolf Optimizer

Locally Weighted Non-Parametric Modeling of Ship Maneuvering Motion Based on Sparse Gaussian Process

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