Bagging-based neural network ensemble for load identification with parameter sensitivity considered

Hu, Xinyuan; Zeng, Yuan; Qin, Chao; Meng, Dezhuang

doi:10.1016/j.egyr.2022.08.056

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Compared with statistical, machine learning and deep learning methods, XEWNet performs better in 75% of the short-term and long-term predicted cases of dengue fever incidence rate [19] . A neural network ensemble method considering parameter sensitivity is proposed to solve the problem of convergence and relatively low accuracy of training [20] .…”

Section: Introductionmentioning

confidence: 99%

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Zhang

Zhou

et al. 2023

j. of Comput. sci. res.

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Precipitation is a significant index to measure the degree of drought and flood in a region, which directly reflects the local natural changes and ecological environment. It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy. In order to accurately predict precipitation, a new precipitation prediction model based on extreme learning machine ensemble (ELME) is proposed. The integrated model is based on the extreme learning machine (ELM) with different kernel functions and supporting parameters, and the submodel with the minimum root mean square error (RMSE) is found to fit the test data. Due to the complex mechanism and factors affecting precipitation change, the data have strong uncertainty and significant nonlinear variation characteristics. The mean generating function (MGF) is used to generate the continuation factor matrix, and the principal component analysis technique is employed to reduce the dimension of the continuation matrix, and the effective data features are extracted. Finally, the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June, July and August, and a comparative experiment is carried out by using ELM, long-term and short-term memory neural network (LSTM) and back propagation neural network based on genetic algorithm (GA-BP). The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models, and it has high stability and reliability, which provides a reliable method for precipitation prediction.

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Section: Introductionmentioning

confidence: 99%

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Zhang

Zhou

et al. 2023

j. of Comput. sci. res.

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“…At the same time, it can effectively avoid over-fitting by reducing the variance of the base classifier [13]. The strategy of Bagging has been used by many deep ensemble learning networks to deal with various technical problems such as fuel cell optimization [14], load detection [15], water quality detection [16] and medicine [17][18]. In [19], Imran Ul Haq et al propose a new deep convolution neural network based on feature fusion and ensemble learning strategy to improve the abnormality detection in mammography.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Network for Handwritten Digit Recognition Based on Fusion Optimized CNN

Li Cui,

Ting-Xuan Chen

et al. 2023

Journal of Computers

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<p>Handwritten digit recognition is an active research field. These recognition systems are faced with many challenges, including accuracy, speed and automatic extraction of complex handwriting features. In this paper, a Stacking ensemble learning model based on fusion optimized CNN is proposed, which can be effectively used for handwritten digit recognition. To better extract the features of complex handwritten digital images and maximize the reliability of the model, the Bagging strategy combined with six CNNs is used for feature extraction for the first time, and SVM is used for classification. This not only improves the accuracy and stability of the model, but also effectively avoids over-fitting. In addition, a fusion optimization algorithm based on Adam and SGD is proposed to solve the problem that CNN falls into local optimum due to a large number of iterations. During the process of training, ASCNN can not only speed up the convergence rate in the early stage, but also reduce the oscillation phenomenon in the late stage. Extensive experimental results on the well-known MNIST and USPS handwriting image datasets demonstrate the effectiveness of the proposed model.</p> <p> </p>

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Neural Network-Based Load Identification for Residential Electrical Installations. A Review and an Online Experimental Application

Sonck-Martinez,

Rodríguez-Mata,

Medrano-Hermosillo

et al. 2023

Communications in Computer and Information Science

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Bagging-based neural network ensemble for load identification with parameter sensitivity considered

Cited by 4 publications

References 6 publications

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Ensemble Learning Network for Handwritten Digit Recognition Based on Fusion Optimized CNN

Neural Network-Based Load Identification for Residential Electrical Installations. A Review and an Online Experimental Application

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