A delay prediction model for high-speed railway: an extreme learning machine tuned via particle swarm optimization

2020 ◽  
Author(s):  
Yanqiu Li ◽  
Xinyue Xu ◽  
Jianmin Li ◽  
Rui Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
High Speed Railway ◽  
Learning Machine ◽  
Delay Prediction

scholarly journals Improve Efficiency of Extreme Learning Machine based on Chaos Particle Swarm Optimization Method

2019 ◽  
Vol 8 (03) ◽  
pp. 24491-24501
Author(s):  
Yuwen Pan Zhan Wen ◽  
Yahui Chen, Wenzao Li
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Particle Swarm ◽  
Optimization Method ◽  
Running Speed ◽  
Swarm Optimization ◽  
Fast Training ◽  
Learning Machine ◽  
Hidden Layer

Extreme Learning Machine (ELM) and Regularized Extreme Learning Machine (RELM) have advantages of fast training speed and good generalization. However, ELM/RELM often needs numerous number of hidden layer nodes to get better performance. The superabundant nodes in hidden layer maybe lead to low running speed. Thus it is not feasible to use ELM in some fields that require high speed algorithms. Therefore, in this paper, we propose an Improved ELM/RELM Optimized based on Chaos Particle Swarm Optimization (CPSO-ELM/RELM) to reduce the number of hidden layer nodes, but still maintain a desirable accuracy. At the same time, it lowers the running speed compared with other algorithms. To verify the application of this method, we design numerous experiments for ELM and RRELM. Their simulation shows that the approach improves the speed of the algorithms, and the accuracy is still high. This makes it possible to use improved CPSO-ELM/RELM in some system with high real-time requirements.

scholarly journals Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Xue-cun Yang ◽  
Xiao-ru Yan ◽  
Chun-feng Song
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Coal Slurry ◽  
Swarm Optimization ◽  
Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

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