Using a kernel extreme learning machine with grey wolf optimization to predict the displacement of step-like landslide

2019 ◽  
Vol 79 (2) ◽  
pp. 673-685 ◽  
Author(s):  
Kang Liao ◽  
Yiping Wu ◽  
Fasheng Miao ◽  
Linwei Li ◽  
Yang Xue
Keyword(s):  
Extreme Learning Machine ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

scholarly journals An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Qiang Li ◽  
Huiling Chen ◽  
Hui Huang ◽  
Xuehua Zhao ◽  
ZhenNao Cai ◽  
...  
Keyword(s):  
Feature Selection ◽  
Extreme Learning Machine ◽  
Medical Diagnosis ◽  
Feature Subset ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Kernel Extreme Learning Machine ◽  
Optimal Feature Subset ◽  
Learning Machine ◽  
Optimal Feature

In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

scholarly journals Underwater Acoustic Signal Prediction Based on MVMD and Optimized Kernel Extreme Learning Machine

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Hong Yang ◽  
Lipeng Gao ◽  
Guohui Li
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Acoustic Signal ◽  
Grey Wolf ◽  
Underwater Acoustic ◽  
Signal Prediction ◽  
Mode Decomposition ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Underwater Acoustic Signal

Aiming at the chaotic characteristics of underwater acoustic signal, a prediction model of grey wolf-optimized kernel extreme learning machine (OKELM) based on MVMD is proposed in this paper for short-term prediction of underwater acoustic signals. To solve the problem of K value selection in variational mode decomposition, a new K value selection method MVMD is proposed from the perspective of mutual information, which avoids the blindness of variational mode decomposition (VMD) in the preset modal number. Based on the prediction model of kernel extreme learning machine (KELM), this paper uses grey wolf optimization (GWO) algorithm to optimize and select its regularization parameters and kernel parameters and proposes an optimized kernel extreme learning machine OKELM. To further improve the prediction performance of the model, combined with MVMD, an underwater acoustic signal prediction model based on MVMD-OKELM is established. MVMD-OKELM prediction model is applied to Mackey–Glass chaotic time series prediction and underwater acoustic signal prediction and is compared with ARIMA, EMD-OKELM, and other prediction models. The experimental results show that the proposed MVMD-OKELM prediction model has a higher prediction accuracy and can be effectively applied to the prediction of underwater acoustic signal series.

scholarly journals Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qiao Shi-fan ◽  
Tan Jun-kun ◽  
Zhang Yong-gang ◽  
Wan Li-jun ◽  
Zhang Ming-fei ◽  
...  
Keyword(s):  
Time Series ◽  
Extreme Learning Machine ◽  
Absolute Error ◽  
Ground Subsidence ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Foundation Pit ◽  
Machine Model ◽  
Model Based ◽  
Learning Machine

This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability.

scholarly journals Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 109 ◽  
Author(s):  
Jiale Ding ◽  
Guochu Chen ◽  
Kuo Yuan
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Percentage Error ◽  
Grey Wolf ◽  
Power Prediction ◽  
Grey Wolf Optimization ◽  
Short Term ◽  
Wind Power Prediction ◽  
Learning Machine

In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.

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