Predicting strip thickness of hot rolled sheet based on the combination of correlation analysis and Extreme Learning Machine

Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of representing uncertainty. In this paper, an integration scheme mainly composed of correlation analysis and improved weighted extreme learning machine is proposed for probabilistic load forecasting. In this scheme, a novel cooperation of wavelet packet transform and correlation analysis is developed to deal with the data noise. Meanwhile, an improved weighted extreme learning machine with a new switch algorithm is provided to effectively obtain stable forecasting results. The probabilistic forecasting task is then accomplished by generating the confidence intervals with the Gaussian process. The proposed integration scheme, tested by actual data from Global Energy Forecasting Competition, is proved to have a better performance in graphic and numerical results than the other available methods.

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A Strip Thickness Prediction Algorithm Using Extreme Learning Machine with Improved PSO

Journal of Information and Computational Science ◽

10.12733/jics20105097 ◽

2014 ◽

Vol 11 (18) ◽

pp. 6763-6773 ◽

Cited By ~ 1

Author(s):

Li Zhang

Keyword(s):

Extreme Learning Machine ◽

Strip Thickness ◽

Prediction Algorithm ◽

Thickness Prediction ◽

Learning Machine ◽

Improved Pso

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Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis

International Journal of Intelligent Systems and Applications ◽

10.5815/ijisa.2021.03.02 ◽

2021 ◽

Vol 13 (3) ◽

pp. 11-22

Author(s):

Kasliono ◽

◽

Suprapto ◽

Faizal Makhrus

Keyword(s):

Correlation Analysis ◽

Extreme Learning Machine ◽

Machine Method ◽

Traffic Lights ◽

Traffic Violations ◽

Average Running Time ◽

Vehicle Mobility ◽

Optimum Model ◽

Learning Machine

Traffic is a medium to move from one point to another. Therefore, the role of traffic is very important to support vehicle mobility. If congestion occurs, mobility will be hampered so that it gives influence to other sectors such as financial, air pollution and traffic violations. This study aims to create a model to predict vehicle queue at the traffic lights when its status is red. The prediction is conducted by using Neural Network with Extreme Learning Machine method to predict the length of the vehicle queue, and Correlation Analysis was used to measure the correlation between the connected roads. The conducted experiments use data of the length of the vehicle queue at the traffic lights which was obtained from DISHUB (Transportation Bureau) DI Yogyakarta. Several experiments were carried out to determine the optimum prediction model of vehicle queue length. The experiments found that the optimum model had an average MAPE value of 15.5882% and an average Running Time of 5.2226 seconds.

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