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 Global Solar Radiation Prediction Using Hybrid Online Sequential Extreme Learning Machine Model

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3415 ◽  
Author(s):  
Muzhou Hou ◽  
Tianle Zhang ◽  
Futian Weng ◽  
Mumtaz Ali ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Information Criterion ◽  
Absolute Error ◽  
Global Solar Radiation ◽  
Machine Model ◽  
Learning Machine

Accurate global solar radiation prediction is highly essential for related research on renewable energy sources. The cost implication and measurement expertise of global solar radiation emphasize that intelligence prediction models need to be applied. On the basis of long-term measured daily solar radiation data, this study uses a novel regularized online sequential extreme learning machine, integrated with variable forgetting factor (FOS-ELM), to predict global solar radiation at Bur Dedougou, in the Burkina Faso region. Bayesian Information Criterion (BIC) is applied to build the seven input combinations based on speed (Wspeed), maximum and minimum temperature (Tmax and Tmin), maximum and minimum humidity (Hmax and Hmin), evaporation (Eo) and vapor pressure deficiency (VPD). For the difference input parameters magnitudes, seven models were developed and evaluated for the optimal input combination. Various statistical indicators were computed for the prediction accuracy examination. The experimental results of the applied FOS-ELM model demonstrated a reliable prediction accuracy against the classical extreme learning machine (ELM) model for daily global solar radiation simulation. In fact, compared to classical ELM, the FOS-ELM model reported an enhancement in the root mean square error (RMSE) and mean absolute error (MAE) by (68.8–79.8%). In summary, the results clearly confirm the effectiveness of the FOS-ELM model, owing to the fixed internal tuning parameters.

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.

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