scholarly journals Power load prediction method based on kernel extreme learning machine with t-distribution variation bat algorithm

2019 ◽  
Vol 631 ◽  
pp. 042064
Author(s):  
Caijuan Qi ◽  
Qian Zhang ◽  
Xing Tian ◽  
Kun Zhang ◽  
Wei Tang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Method ◽  
Bat Algorithm ◽  
Load Prediction ◽  
Power Load ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
T Distribution

scholarly journals Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

2020 ◽  
Vol 309 ◽  
pp. 04018
Author(s):  
Guangjie Hao ◽  
Menghong Yu ◽  
Zhen Su
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Prediction Method ◽  
Bat Algorithm ◽  
Yangtze Estuary ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Density Prediction ◽  
Learning Machine ◽  
Selection Of

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

scholarly journals A novel framework for prediction of dam deformation based on extreme learning machine and Lévy flight bat algorithm

2021 ◽  
Author(s):  
Youliang Chen ◽  
Xiangjun Zhang ◽  
Hamed Karimian ◽  
Gang Xiao ◽  
Jinsong Huang
Keyword(s):  
Extreme Learning Machine ◽  
Kernel Function ◽  
Bat Algorithm ◽  
Deformation Monitoring ◽  
Gaussian Kernel ◽  
Lévy Flight ◽  
Levy Flight ◽  
Kernel Extreme Learning Machine ◽  
Dam Deformation ◽  
Learning Machine

Abstract Dam deformation monitoring and prediction are crucial for evaluating the safety of reservoirs. There are several elements that influence dam deformation. However, the mixed effects of these elements are not always linear. Oppose to a single-kernel extreme learning machine, which suffers from poor generalization performance and instability, in this study, we proposed an improved bat algorithm for dam deformation prediction based on a hybrid-kernel extreme learning machine. To improve the learning ability of the global kernel and the generalization ability of the local kernel, we combined the global kernel function (polynomial kernel function) and local kernel function (Gaussian kernel function). Moreover, a Lévy flight bat optimization algorithm (LBA) was proposed to overcome the shortages of bat algorithms. The results showed that our model outperformed other models. This proves that our proposed algorithm and methods can be used in dam deformation monitoring and prediction in different projects and regions.

scholarly journals Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches

2021 ◽  
Author(s):  
Roghayeh Ghasempour ◽  
Kiyoumars Roushangar ◽  
Parveen Sihag
Keyword(s):  
Suspended Sediment ◽  
Extreme Learning Machine ◽  
Mississippi River ◽  
Flow Characteristics ◽  
Gaussian Process Regression ◽  
Accurate Estimation ◽  
Post Processing ◽  
Load Prediction ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Abstract Sediment transportation and accurate estimation of its rate is a significant issue for the river engineers and researchers. In this study, the capability of kernel based approaches including Kernel Extreme Learning Machine (KELM) and Gaussian Process Regression (GPR) was assessed for predicting the river daily Suspended Sediment Discharge (SSD). For this aim, Mississippi river with three consecutive hydrometric stations was selected as the case study. Based on the sediment and flow characteristics during the period of 2005–2008 several models were developed and tested under two scenarios (i.e. modeling based on each station's own data or previous stations' data). Two post-processing techniques namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD) were used for enhancing the SSD modeling capability. Also, data post-proceeding was done using Simple Linear Averaging (SLAM) and Nonlinear Kernel Extreme Learning Machine Ensemble (NKELME) methods. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the models capability up to 35%. It was found that the SSD modeling based on station's own data led to better results; however, using the integrated approaches the previous stations data could be applied successfully for the SSD modeling when station's own data were not available.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

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