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

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.

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.

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 Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

2019 ◽  
Vol 9 (20) ◽  
pp. 4215 ◽  
Author(s):  
Zhengmin Kong ◽  
Zhou Xia ◽  
Yande Cui ◽  
He Lv
Keyword(s):  
Correlation Analysis ◽  
Extreme Learning Machine ◽  
Load Forecasting ◽  
Integration Scheme ◽  
Electric Load ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Weighted Extreme Learning Machine ◽  
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.

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

Classification of catenary equipment based on a coupled genetic algorithm-extreme learning machine method

2020 ◽  
Vol 62 (1) ◽  
pp. 15-21
Author(s):  
Changdong Wu
Keyword(s):  
Genetic Algorithm ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Classification Accuracy ◽  
Activation Function ◽  
Machine Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Low Dimensional

In an online monitoring system for an electrified railway, it is important to classify the catenary equipment successfully. The extreme learning machine (ELM) is an effective image classification algorithm and the genetic algorithm (GA) is a typical optimisation method. In this paper, a coupled genetic algorithm-extreme learning machine (GA-ELM) technique is proposed for the classification of catenary equipment. Firstly, the GA is used to search for optimal features by reducing the initial multi-dimensional features to low-dimensional features. Next, the optimised features are used as the input to the ELM. The ELM algorithm is then used to classify the catenary equipment. In this process, the impacts of the activation function, the number of hidden layer neurons and different models on the performance of the ELM are discussed in turn. Finally, the proposed method is compared with traditional methods in terms of classification accuracy and efficiency. Experimental results show that the number of feature dimensions decreases to 58% of the original number and the computational complexity is greatly decreased. Moreover, the reduced features and the few steps of the ELM improve the classification accuracy and speed. Noticeably, when the performance of the GA-ELM method is compared with that of the ELM method, the classification accuracy rate is 93.33% compared with 85.83% and the time consumption is 2.25 s compared with 8.85 s, respectively. That is to say, the proposed method not only decreases the number of features but also increases the classification accuracy and efficiency. This meets the needs of a real-time online condition monitoring system.

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