scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

scholarly journals Wavefront Restoration Technology of Dynamic Non-Uniform Intensity Distribution Based on Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3877
Author(s):  
Haiqi Lin ◽  
Xing He ◽  
Shuai Wang ◽  
Ping Yang
Keyword(s):  
Root Mean Square Error ◽  
Extreme Learning Machine ◽  
Intensity Distribution ◽  
Near Field ◽  
Wavefront Sensor ◽  
Mean Square ◽  
Initial Value ◽  
Machine Method ◽  
Simulation Results ◽  
Learning Machine

Non-uniform intensity distribution of laser near-field beam results in the irregular shape of the spot in the wavefront sensor. The intensity of some sub-aperture spots may be too weak to be detected, and the accuracy of wavefront restoration is seriously affected. Therefore, an extreme learning machine method is proposed to realize high precision wavefront restoration under dynamic non-uniform intensity distribution. The simulation results show that this method has better accuracy of wavefront restoration than the classical modal algorithm under dynamic non-uniform intensity distribution. The root mean square error of the residual wavefront for the proposed method is only 2.9% of the initial value.

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

2018 ◽  
Vol 281 ◽  
pp. 209-221 ◽  
Author(s):  
Irfan Bahiuddin ◽  
Saiful A. Mazlan ◽  
Mohd. I. Shapiai ◽  
Seung-Bok Choi ◽  
Fitrian Imaduddin ◽  
...  
Keyword(s):  
Constitutive Model ◽  
Extreme Learning Machine ◽  
Machine Method ◽  
Learning Machine ◽  
Magneto Rheological

scholarly journals Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4033
Author(s):  
Jonas Bielskus ◽  
Violeta Motuzienė ◽  
Tatjana Vilutienė ◽  
Audrius Indriulionis
Keyword(s):  
Energy Efficiency ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Open Space ◽  
Prediction Models ◽  
Co2 Concentration ◽  
Full Potential ◽  
Machine Model ◽  
Learning Machine ◽  
Occupancy Prediction

Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

scholarly journals A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Pengbo Zhang ◽  
Zhixin Yang
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Approximation Accuracy ◽  
Weak Learner ◽  
Fast Learning ◽  
Learning Speed ◽  
Regression Problems ◽  
Learning Machine ◽  
The Stability ◽  
Stability And Accuracy

Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner’s performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of “weak” learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.

scholarly journals Short-Term Electric Load and Price Forecasting Using Enhanced Extreme Learning Machine Optimization in Smart Grids

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 866 ◽  
Author(s):  
Aqdas Naz ◽  
Muhammad Javed ◽  
Nadeem Javaid ◽  
Tanzila Saba ◽  
Musaed Alhussein ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Mean Square Error ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Computational Time ◽  
Major Constituent ◽  
Smart Devices ◽  
Mean Square ◽  
Simulation Results ◽  
Learning Machine

A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dramatically. The forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of SG. Various learning algorithms have been proposed to solve the forecasting problem. The sole purpose of this work is to predict the price and load efficiently. The first technique is Enhanced Logistic Regression (ELR) and the second technique is Enhanced Recurrent Extreme Learning Machine (ERELM). ELR is an enhanced form of Logistic Regression (LR), whereas, ERELM optimizes weights and biases using a Grey Wolf Optimizer (GWO). Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. The simulations are performed on two different datasets. The first dataset, i.e., UMass Electric Dataset is multi-variate while the second dataset, i.e., UCI Dataset is uni-variate. The first proposed model performed better with UMass Electric Dataset than UCI Dataset and the accuracy of second model is better with UCI than UMass. The prediction accuracy is analyzed on the basis of four different performance metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed techniques are then compared with four benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperformed benchmark schemes. The proposed techniques efficiently increased the prediction accuracy of load and price. However, the computational time is increased in both scenarios. ELR achieved almost 5% better results than Convolutional Neural Network (CNN) and almost 3% than LR. While, ERELM achieved almost 6% better results than ELM and almost 5% than RELM. However, the computational time is almost 20% increased with ELR and 50% with ERELM. Scalability is also addressed for the proposed techniques using half-yearly and yearly datasets. Simulation results show that ELR gives 5% better results while, ERELM gives 6% better results when used for yearly dataset.

scholarly journals Development of a Kernel Extreme Learning Machine Model for Capacity Selection of Distributed Generation Considering the Characteristics of Electric Vehicles

2019 ◽  
Vol 9 (12) ◽  
pp. 2401
Author(s):  
Zhongdong Yin ◽  
Jingjing Tu ◽  
Yonghai Xu
Keyword(s):  
Extreme Learning Machine ◽  
Distributed Generation ◽  
Electric Vehicles ◽  
Distribution Network ◽  
Support Vector ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
The Stability ◽  
Capacity Selection ◽  
Selection Of

The large-scale access of distributed generation (DG) and the continuous increase in the demand of electric vehicle (EV) charging will result in fundamental changes in the planning and operating characteristics of the distribution network. Therefore, studying the capacity selection of the distributed generation, such as wind and photovoltaic (PV), and considering the charging characteristic of electric vehicles, is of great significance to the stability and economic operation of the distribution network. By using the network node voltage, the distributed generation output and the electric vehicles’ charging power as training data, we propose a capacity selection model based on the kernel extreme learning machine (KELM). The model accuracy is evaluated by using the root mean square error (RMSE). The stability of the network is evaluated by voltage stability evaluation index (Ivse). The IEEE33 node distributed system is used as simulation example, and gives results calculated by the kernel extreme learning machine that satisfy the minimum network loss and total investment cost. Finally, the results are compared with support vector machine (SVM), particle swarm optimization algorithm (PSO) and genetic algorithm (GA), to verify the feasibility and effectiveness of the proposed model and method.

scholarly journals An Improved QZSS Satellite Clock Offsets Prediction Based on the Extreme Learning Machine Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 156557-156568
Author(s):  
Lina He ◽  
Hairui Zhou ◽  
Shaolin Zhu ◽  
Ping Zeng
Keyword(s):  
Extreme Learning Machine ◽  
Satellite Clock ◽  
Machine Method ◽  
Learning Machine
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