Forecast Application of Time Series Model Based on BLS in Port Cargo Throughput

Yang, Jingjing; Li, Tieshan; Zuo, Yi; Tian, Ye; Cao, Yuchi; Yang, He; Chen, C. L. Philip

doi:10.1109/spac46244.2018.8965603

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Compared with LASSO regression, RNN and other machine learning models, BLS was concluded to significantly improve the forecast speed [25]. Yang et al proposed a novel time-series model based on BLS to forecast port throughput, which exhibited superior performance to that of classical-time series models [26].…”

Section: Regression Analysis Using Blsmentioning

confidence: 99%

Application of Regression Analysis Using Broad Learning System for Time-Series Forecast of Ship Fuel Consumption

Zuo

Jiang

2022

Sustainability

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Accurately forecasting the fuel consumption of ships is critical for improving their energy efficiency. However, the environmental factors that affect ship fuel consumption have not been researched comprehensively, and most of the relevant studies continue to present efficiency and accuracy issues. In view of such problems, a time-series forecasting model of ship fuel consumption based on a novel regression analysis using broad learning system (BLS) was developed in this study. The BLS was compared to a diverse set of fuel consumption forecasting models based on time-series analyses and machine learning techniques, including autoregressive integrated moving average model with exogenous inputs (ARIMAX), support vector regression (SVR), recurrent neural network (RNN), long short-term memory network (LSTM), and extreme learning machines (ELM). In the experiment, two types of passenger roll-on roll-off (ro-ro) ship and liquefied petroleum gas (LPG) carrierwere used as research objects to verify the proposed method’s generalizability, with data divided among two groups (RM, RB). The experimental results showed that the BLS model is the best choice to forecast fuel consumption in actual navigation, with mean absolute error (MAE) values of 0.0140 and 0.0115 on RM and RB, respectively. For the LPG carrier, it has also been proven that the forecast effect is improved when factoring the sea condition, with MAE reaching 0.0108 and 0.0142 under ballast and laden conditions, respectively. Furthermore, the BLS features the advantages of low computing complexity and short forecast time, making it more suitable for real-world applications. The results of this study can therefore effectively improve the energy efficiency of ships by reducing operating costs and emissions.

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Section: Regression Analysis Using Blsmentioning

confidence: 99%

Application of Regression Analysis Using Broad Learning System for Time-Series Forecast of Ship Fuel Consumption

Zuo

Jiang

2022

Sustainability

Self Cite

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“…At the same time, increasing the data of the enhanced node can increase the complexity of the model and meet the complexity requirements of the model. Yang et al 21 used BLS to predict and analyze the throughput of port containers, and the results showed that compared with traditional time series-based models, using BLS has higher accuracy. Based on the previously proposed BLS, Chen et al 22 carried out a mathematical proof of its universal approximation properties.…”

Section: Proposed a De-elm Algorithm Combining Differential Evolution (De) Algorithm And Extremementioning

confidence: 99%

Prediction of yarn unevenness based on BMNN

Jiang

Song

Zhang

et al. 2021

Journal of Engineered Fibers and Fabrics

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With the continuous development of deep learning, due to the complexity of the deep neural network structure and the limitation of training time, some scholars have proposed broad learning, the Broad Learning System (BLS). However, BLS currently only verifies that it has excellent effects on some of the network training data sets, and it does not necessarily have excellent effects on some actual data sets. In response to this, this paper uses the effect of BLS in predicting the unevenness of yarn quality in the yarn data set, and proposes a BLS-based multi-layer neural network (MNN) for the problems, which is called Broad Multilayer Neural Network (BMNN).

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