818
Views
1
CrossRef citations to date
0
Altmetric
Research Article

An online self-adaptive RBF network algorithm based on the Levenberg-Marquardt algorithm

, , &
Article: 2146800 | Received 20 Jul 2022, Accepted 04 Nov 2022, Published online: 21 Nov 2022

References

  • Arif, J., N. Ray Chaudhuri, S. Ray, et al. 2009. Online Levenberg-Marquardt algorithm for neural network based estimation and control of power systems. 2009 International Joint Conference on Neural Networks. Atlanta, GA, USA, 199–3809. IEEE.
  • Atif, S. M., S. Khan, I. Naseem, R. Togneri, and M. Bennamoun. 2022. Multi-kernel fusion for RBF neural networks. Neural Processing Letters. doi:10.1007/s11063-022-10925-3.
  • Gao, X. 2022. A nonlinear prediction model for Chinese speech signal based on RBF neural network. Multimedia Tools and Applications 81:5033–49. doi:10.1007/s11042-021-11612-6.
  • Gu, L., D. K. S. Tok, and D.L. Yu. 2018. Development of adaptive p-step RBF network model with recursive orthogonal least squares training. Neural Compution and Applications 29 (5):1445–54. doi:10.1007/s00521-016-2669-x.
  • Hao, Y., P. D. Reiner, T. Xie, T. Bartczak, and B. M. Wilamowski. 2014. An incremental design of radial basis function networks. IEEE Transactions on Neural Networks and Learning Systems 25 (10):1793–803. doi:10.1109/TNNLS.2013.2295813.
  • Harpham, C., and C. W. Dawson. 2006. The effect of different basis functions on a radial basis function network for time series prediction: A comparative study. Neurocomputing 69 (16–18):2161–70. doi:10.1016/j.neucom.2005.07.010.
  • Houcine Bergou, E., Y. Diouane, and V. Kungurtsev. 2020. Convergence and complexity analysis of a Levenberg–Marquardt algorithm for inverse problems. Journal of Optimization Theory and Applications 185 (3):1–18. doi:10.1007/s10957-020-01666-1.
  • Jia, L., W. Li, and J. Qiao. 2022. An online adjusting RBF neural network for nonlinear system modeling. Application Intelligence. Advanced online publication. doi: 10.1007/s10489-021-03106-7.
  • Jiang, Q., L. Zhu, C. Shu, and V. Sekar. 2022. An efficient multilayer RBF neural network and its application to regression problems. Neural Computing & Applications 34 (6):4133–50. doi:10.1007/s00521-021-06373-0.
  • Kadakadiyavar, S., N. Ramrao, and M. K. Singh. 2020. Efficient mixture control chart pattern recognition using adaptive RBF neural network. International Journal of Information Technology 12 (4):1271–80. doi:10.1007/s41870-019-00381-z.
  • Khan, S., I. Naseem, R. Togneri, and M. Bennamoun. 2017. A novel adaptive kernel for the RBF neural networks. Circuits Systems and Signal Processing 36 (4):1639–53. doi:10.1007/s00034-016-0375-7.
  • La Rosa Centeno, L., F. C. C. De Castro, M. C. F. De Castro, C. Müller, and S. M. Ribeiro. 2018. Cognitive radio signal classification based on subspace decomposition and RBF neural networks. Wireless Networks 24 (3):821–31. doi:10.1007/s11276-016-1376-y.
  • Li, S., Q. Chen, and G.-B. Huang. 2006. Dynamic temperature modeling of continuous annealing furnace using GGAP-RBF neural network. Neurocomputing 69 (4–6):523–36. doi:10.1016/j.neucom.2005.01.008.
  • Lu, Y. W., N. Sundararajan, and P. Saratchandran. 1997. A sequential learning scheme for function approximation using minimal radial basis function neural networks. Neural computation 9 (2):461–78. doi:10.1162/neco.1997.9.2.461.
  • Meng, X., Y. Zhang, and J. Qiao. 2021. An adaptive task-oriented RBF network for key water quality parameters prediction in wastewater treatment process. Neural Computing & Applications 33 (17):11401–14. doi:10.1007/s00521-020-05659-z.
  • Pedro, M. F., and A. E. Ruano. 2009. Online sliding-window methods for process model adaptation. IEEE Transactions Instrumentation and Measurement 58 (9):3012–20. doi:10.1109/TIM.2009.2016818.
  • Platt, J. 1991. A resource-allocating network for function interpolation. Neural computation 3 (2):213–25. doi:10.1162/neco.1991.3.2.213.
  • Qiao, J., Z. Zhang, and Y. Bo. 2012. An online self-adaptive modular neural network for time-varying systems. Neurocomputing 125:7–16. doi:10.1016/j.neucom.2012.09.038.
  • Rong, H.-J., N. Sundararajan, G.-B. Huang, and P. Saratchandran. 2006. Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets Systems 157 (9):1260–75. doi:10.1016/j.fss.2005.12.011.
  • Wilamowski, B. M., and H. Yu. 2010. Improved computation for Levenberg-Marquardt training. IEEE Transactions on Neural Networks 21 (6):930–37. doi:10.1109/TNN.2010.2045657.
  • Xi, M., P. Rozycki, J.-F. Qiao, and B. M. Wilamowski. 2018. Nonlinear system modeling using RBF networks for industrial application. IEEE Transactions on Industrial Informatics 14 (3):931–40. doi:10.1109/TII.2017.2734686.
  • Yingwei, L., N. Sundararajan, and P. Saratchandran. 1997. Identification of time-varying nonlinear systems using miniman radial basis function neural networks. IEEE Proceedings-Control Theory Applications 144 (2):202–08. doi:10.1049/ip-cta:19970891.
  • Zhang, Y., D. Kim, Y. Zhao, and J. Lee. 2020. PD control of a manipulator with gravity and inertia compensation using an RBF neural network. International Journal of Control, Automation, and Systems 18 (12):3083–92. doi:10.1007/s12555-019-0482-x.
  • Zhou, K., S.K. Oh, and J. Qiu. 2022. Design of ensemble fuzzy-RBF neural networks based on feature extraction and multi-feature fusion for GIS partial discharge recognition and classification. Journal of Electrical Engineering & Technology 17:513–32. doi:10.1007/s42835-021-00941-z.