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- Title
A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization.
- Authors
Zheng, Xiaoliang; Lai, Wenhao; Chen, Hualiang; Fang, Shen; Li, Ziqiao
- 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–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
FORECASTING; PARTICLE swarm optimization; GAUSSIAN function; MACHINE learning; KERNEL functions
- Publication
Applied Sciences (2076-3417), 2020, Vol 10, Issue 10, p3517
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app10103517