MIMO non-parametric modeling of ship maneuvering motion for marine simulator using adaptive moment estimation locally weighted learning

Zhang, Zhao; Ren, Junsheng; Bai, Weiwei

doi:10.1016/j.oceaneng.2022.112103

Cited by 9 publications

(1 citation statement)

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“…Parameter estimation for multivariate systems has taken a considerable role in system identification and has attracted much attention from researchers in recent decades. Multivariate systems are more difficult to identify than scalar systems because they have more unknown parameters, are accompanied by more complex random interferences, and the parameters of some channels are coupled [5][6][7]. Good identification results are often not achieved if the identification approaches for scalar systems are applied to multivariate systems without modification.…”

Section: Introductionmentioning

confidence: 99%

A Filtering-Based Stochastic Gradient Estimation Method for Multivariate Pseudo-Linear Systems Using the Partial Coupling Concept

Ma,

Liu,

Chen

2023

Processes

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Solutions for enhancing parameter identification effects for multivariate equation-error systems in random interference and parameter coupling conditions are considered in this paper. For the purpose of avoiding the impact of colored noises on parameter identification precision, an appropriate filter is utilized to process the autoregressive moving average noise. Then, the filtered system is transformed into a number of sub-identification models based on system output dimensions. Founded on negative gradient search, a new multivariate filtering algorithm employing a partial coupling approach is proposed, and a conventional gradient algorithm is derived for comparison. Parameter identification for multivariate equation-error systems has a high estimation accuracy and an efficient calculation speed with the application of the partial coupling approach and the data filtering method. Two simulations are performed to reveal the proposed method’s effectiveness.

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Section: Introductionmentioning

confidence: 99%