1. Salamon, S. J., H. J. Hansen, and H. D. Abbott, "Modelling radio refractive index in the atmospheric surface layer," Electronics Letters, Vol. 51, No. 14, 1119-1121, 2015.
doi:10.1049/el.2015.0195
2. Bean, B. R. and G. D. Thayer, "Models of the atmospheric radio refractive index," Proceedings of the IRE, Vol. 47, No. 5, 740-755, 1959.
doi:10.1109/JRPROC.1959.287242
3. Hopfield, H. S., "Two-quartic tropospheric refractivity profile for correcting satellite data," Journal of Geophysical Research, Vol. 74, No. 18, 4487-4499, 1969.
doi:10.1029/JC074i018p04487
4. Anthony, R. L., R. Chris, V. S. Sergey, and D. A. Kenneth, "Anderson vertical profiling of atmospheric refractivity from ground-based GPS," Radio Science, Vol. 137, No. 3, 1-21, 2002.
5. Wu, Y. Y., Z. J. Hong, P. Guo, and J. Zhang, "Simulation of atmospheric refractive profile retrieving from low-elevation ground-based GPS observations," Chinese Journal of Geophysics, Vol. 53, No. 4, 639-645, 2010.
doi:10.1002/cjg2.1533
6. Chiou, M.-M. and J.-F. Kiang, "Retrieval of refractivity profile with ground-based radiooccultation by using an improved harmony search algorithm," Progress In Electromagnetics Research M, Vol. 51, 19-31, 2016.
doi:10.2528/PIERM16052505
7. Ibeh, G. F. and G. A. Agbo, "Estimation of tropospheric refractivity with artificial neural network at Minna, Nigeria," Global Journal of Science Frontier Research, Vol. 12, No. 1, 9-14, 2012.
8. Tepecik, C. and I. Navruz, "A novel hybrid model for inversion problem of atmospheric refractivity estimation," AEU — International Journal of Electronics and Communications, Vol. 84, 258-264, 2018.
doi:10.1016/j.aeue.2017.12.009
9. Cai, Y., S. Sun, C. Wang, and C. Gao, "The research on flux linkage characteristic based on BP and RBF neural network for switched reluctance motor," Progress In Electromagnetics Research M, Vol. 35, 151-161, 2014.
doi:10.2528/PIERM14011604
10. Adediji, A. T. and S. T. Ogunjo, "Variations in non-linearity in vertical distribution of microwave radio refractivity," Progress In Electromagnetics Research M, Vol. 36, 177-183, 2014.
doi:10.2528/PIERM14041606
11. Lee, C. M. and C. N. Ko, "Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm," Neurocomputing, Vol. 73, No. 1-3, 449-460, 2009.
doi:10.1016/j.neucom.2009.07.005
12. Dubey, A. D., "K-Means based radial basis function neural networks for rainfall prediction," International Conference on Trends in Automation, Communications and Computing Technology, IEEE, Bangalore, 2015.
13. Addeh, A., A. Khormalib, and N. A. Golilarzc, "Control chart pattern recognition using RBF neural network with new training algorithm and practical features," ISA Transactions, Vol. 79, 202-216, 2018.
doi:10.1016/j.isatra.2018.04.020
14. Kumar, R., S. Srivastava, J. R. P. Gupta, and A. Mohindru, "Temporally local recurrent radial basis function network for modelingand adaptive control of nonlinear systems," ISA Transactions, Vol. 87, 88-115, 2019.
doi:10.1016/j.isatra.2018.11.027
15. Yang, X. P., Y. Q. Li, Y. Z. Sun, L. Teng, and T. K. Sarkar, "Fast and robust RBF neural network based on global K-means clustering with adaptive selection radius for sound source angle estimation," IEEE Transactions on Antennas and Propagation, Vol. 66, No. 6, 3097-3107, 2018.
doi:10.1109/TAP.2018.2820320
16. Zainud-Deen, S. H., H. A. El-Azem Malhat, K. H. Awadalla, and E. S. El-Hada, "Direction of arrival and state of polarization estimation using radial basis function neural network (RBFNN)," Progress In Electromagnetics Research B, Vol. 2, 137-150, 2008.
doi:10.2528/PIERB07111801
17. Chen, D.W., "Research on traffic flow prediction in the big data environment based on the improved RBF neural network," IEEE Transactions on Industrial Informatics, Vol. 13, No. 4, 2000-2008, 2017.
doi:10.1109/TII.2017.2682855
18. Wei, D. F., "Network traffic prediction based on RBF neural network optimized by improved gravitation search algorithm," Neural Computing and Applications, Vol. 28, No. 8, 2303-2312, 2017.
doi:10.1007/s00521-016-2193-z
19. Chen, B. H., S. C. Huang, C. Y. Li, and S. Y. Kuo, "Haze removal using radial basis function networks for visibility restoration applications," IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 8, 3828-3838, 2017.
20. Satapathy, S. K., S. Dehuri, and A. K. Jagadev, "EEG signal classification using PSO trained RBF neural network for epilepsy identification," Informatics in Medicine Unlocked, Vol. 6, 1-11, 2017.
doi:10.1016/j.imu.2016.12.001
21. Kanojia, M. G. and S. Abraham, "Breast cancer detection using RBF neural network," International Conference on Contemporary Computing and Informatics, IEEE, Greater Noida, 2017.
22. Mohamed, M. D. A., E. A. Soliman, and M. A. El-Gamal, "Optimization and characterization of electromagnetically coupled patch antennas using RBF neural networks," Journal of Electromagnetic Waves and Applications, Vol. 20, No. 8, 1101-1114, 2006.
doi:10.1163/156939306776930240
23. Li, H. X., J. H. Chang, F. Xu, B. G. Liu, Z. X. Liu, L. Y. Zhu, and Z. B. Yang, "An RBF neural network approach for retrieving atmospheric extinction coefficients based on lidar measurements," Applied Physics B, Vol. 124, 184, 2018.
doi:10.1007/s00340-018-7055-1
24. Smith, E. K. and S. Weintraub, "The constants in the equation for atmospheric refractive index at Radiofrequencies," Proceedings of the IRE, Vol. 41, No. 8, 1035-1037, 1953.
doi:10.1109/JRPROC.1953.274297
25. Adediji, A. T. and M. O. Malhat, "Vertical profile of radio refractivity gradient in Akure South- West Nigeria," Progress In Electromagnetics Research C, Vol. 4, 157-168, 2008.
26. Demuth, H. and M. Beale, , Neural Network Toolbox for Use With MATLAB. User's Guide 6th edition, The Math Works, Inc., Natick, MA, 2007.