Time Series Prediction Using Radial Basis Function Network with Transformation of Training Data and Its Applications-Reference-Cited by-同舟云学术

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Time Series Prediction Using Radial Basis Function Network with Transformation of Training Data and Its Applications

Published:2022-06 Issue:3 Volume:56 Page:239-252
ISSN:0146-4116
Container-title:Automatic Control and Computer Sciences
language:en
Short-container-title:Aut. Control Comp. Sci.

Author:

Satoshi Kitayama ,Kohei Saito

Publisher

Allerton Press

Subject

Signal Processing,Control and Systems Engineering,Software

Link

https://link.springer.com/content/pdf/10.3103/S0146411622030026.pdf

Reference21 articles.

1. Casdagli, M., Nonlinear prediction if chaotic time series, Phys. D (Amsterdam, Neth.), 1998, vol. 35, no. 3, pp. 335–356. https://doi.org/10.1016/0167-2789(89)90074-2

2. Zhang, W. and Wei, D., Prediction for network traffic of radial basis function neural network model based on improved particle swarm optimization algorithm, Neural Comput. Appl., 2018, vol. 29, pp. 1143–1152. https://doi.org/10.1007/s00521-016-2483-5

3. Cao, L., Support vector machines experts for time series forecasting, Neurocomputing, 2003, vol. 51, pp. 321–339. https://doi.org/10.1016/S0925-2312(02)00577-5

4. Luo, C., Huang, C., Cao, J., Lu, J., Huang, W., Guo, J., and Wei, Y., Short-term traffic flow prediction based on least square support vector machine with hybrid optimization algorithm, Neural Process. Lett., 2019, vol. 50, pp. 2305–2322. https://doi.org/10.1007/s11063-019-09994-8

5. Cowper, M.R., Mulgrew, B., and Unsworth, C.P., Nonlinear prediction of chaotic signals using a normalised radial basis function network, Signal Process., 2002, vol. 82, pp. 775–789. https://doi.org/10.1016/S0165-1684(02)00155-X

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