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A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
- Source :
- Applied Sciences, Volume 10, Issue 10, Applied Sciences, Vol 10, Iss 3517, p 3517 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- Accurate and efficient prediction of mobile network traffic in a public setting with changing flow of people can not only ensure a stable network but also help operators make resource scheduling decisions before reasonably allocating resources. Therefore, this paper proposes a method based on kernel extreme learning machine (kELM) for traffic data prediction. Particle swarm optimization (PSO), multiverse optimizer (MVO), and moth&ndash<br />flame optimization (MFO) were adopted to optimize kELM parameters for finding the best solution. To verify the predictive performance of the kernel ELM model, backpropagation (BP) neural network, v-support vector regression (vSVR), and ELM were also applied to traffic prediction, and the results were compared with kELM. Experimental results showed that the smallest mean absolute percentage error in the test (11.150%) was achieved when kELM was optimized by MFO with Gaussian as the kernel function, that is, the prediction result of MFO-kELM was the best. This study can provide significant guidance for network stability and resource conservation.
- Subjects :
- Mathematical optimization
Computer science
traffic data prediction
Stability (learning theory)
02 engineering and technology
lcsh:Technology
lcsh:Chemistry
0502 economics and business
kernel extreme learning machine
0202 electrical engineering, electronic engineering, information engineering
parameter optimization
General Materials Science
lcsh:QH301-705.5
Instrumentation
Fluid Flow and Transfer Processes
050210 logistics & transportation
Artificial neural network
lcsh:T
Process Chemistry and Technology
05 social sciences
General Engineering
Cellular traffic
Particle swarm optimization
lcsh:QC1-999
Backpropagation
Computer Science Applications
public setting
Mean absolute percentage error
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Kernel (statistics)
Cellular network
020201 artificial intelligence & image processing
lcsh:Engineering (General). Civil engineering (General)
lcsh:Physics
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....c9921ee1bae7fab8b086b5809f780a67
- Full Text :
- https://doi.org/10.3390/app10103517