Model Parameter Identification and Simulation of Ship Power System Based on Recursive Least Squares Method

Jin, Hu; Hu, Da Bin; Xiao, Jianhua

doi:10.4028/www.scientific.net/amm.220-223.482

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Mathematical models of ships' maneuvering motion include the models mentioned above. Common parameter identification methods include least squares [81,82], Kalman filtering [83,84], support vector machines [85,86], neural networks [87,88], least squares support vector machine methods [89][90][91], particle swarm optimization algorithms [92,93], and Bayesian methods [94,95], among others. Several scholars have conducted in-depth research on this [96][97][98].…”

Section: Ship Extreme Short-term Motion Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

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Section: Ship Extreme Short-term Motion Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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“…Accurate cell models rely on accurate internal model parameters, so accurate parameter identification of PEMFC batteries is a prerequisite for establishing accurate and reliable cell models. However, because the PEMFC parameter identification is a nonlinear problem with multiple variables, multiple peaks, a strong coupling, and limited V-I data measured by the cell, it is arduous to use traditional numerical analysis methods for parameter identification, for example, the least squares method, gradient descent method, and the identification results are not ideal [8]. However, meta-heuristic algorithms (MhAs) are widely used in the field of PEMFC parameter extraction due to their low initial value requirements and global search ability, which can avoid falling into local optimum [9].…”

Section: Introductionmentioning

confidence: 99%

Generalized Regression Neural Network Based Meta-Heuristic Algorithms for Parameter Identification of Proton Exchange Membrane Fuel Cell

He¹,

Zhou²,

Liu³

et al. 2023

Energies

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An accurate parameter extraction of the proton exchange membrane fuel cell (PEMFC) is crucial for establishing a reliable cell model, which is also of great significance for subsequent research on the PEMFC. However, because the parameter identification of the PEMFC is a nonlinear optimization problem with multiple variables, peaks, and a strong coupling, it is difficult to solve this problem using traditional numerical methods. Furthermore, because of insufficient current and voltage data measured by the PEMFC, the precision rate of cell parameter extraction is also very low. The study proposes a parameter extraction method using a generalized regression neural network (GRNN) and meta-heuristic algorithms (MhAs). First of all, a GRNN is used to de-noise and predict the data to solve the problems in the field of PEMFC, which include insufficient data and excessive noise data of the measured data. After that, six typical algorithms are used to extract the parameters of the PEMFC under three operating conditions, namely high temperature and low pressure (HTLP), medium temperature and medium pressure (MTMP), and low temperature and high pressure (LTHP). The last results demonstrate that the application of GRNN can prominently decrease the influence of data noise on parameter identification, and after data prediction, it can greatly enhance the precision rate and reliability of MhAs parameter identification, specifically, under HTLP conditions, the V-I fitting accuracy achieved 99.39%, the fitting accuracy was 99.07% on MTMP, and the fitting accuracy was 98.70%.

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