165
Views
0
CrossRef citations to date
0
Altmetric
Research Article

HLA: a novel hybrid model based on fixed structure and variable structure learning automata

, ORCID Icon, &
Pages 231-256 | Received 26 Feb 2020, Accepted 12 Jul 2021, Published online: 13 Feb 2022

References

  • Aguilar, A. S. (1993). Learning Automata: An alternative to artificial neural networks. In Neuroscience: From Neural Networks to Artificial Intelligence (pp. 326–339). Springer.
  • Beigy, H., Meybodi, M. R., & Menhaj, M. B. (2002). Utilization of fixed structure learning automata for adaptation of learning rate in backpropagation algorithm. Journal of Applied Sciences, 2(4), 437–443. https://scialert.net/abstract/?doi=jas.2002.437.443
  • Feng, S., Guo, H., Yang, J., Xu, Z., & Li, S. (2018). A learning automata-based compression scheme for convolutional neural network. In International Conference in Communications, Signal Processing, and Systems (pp. 42–49). Springer, Singapore.
  • Guo, H., Li, S., Li, B., Ma, Y., & Ren, X. (2017). A new learning automata-based pruning method to train deep neural networks. IEEE Internet of Things Journal, 5(5), 3263–3269. https://doi.org/10.1109/JIOT.2017.2711426
  • Guo, H., Li, S., Qi, K., Guo, Y., & Xu, Z. (2018). Learning automata based competition scheme to train deep neural networks. In IEEE Transactions on Emerging Topics in Computational Intelligence, 4(2), 151-158, https://doi.org/10.1109/TETCI.2018.2868474.
  • Guo, H., Wang, S., Fan, J., & Li, S. (2019). Learning automata based incremental learning method for deep neural networks, In IEEE Access, 7, 41164-41171. https://doi.org/10.1109/ACCESS.2019.2907645Learning automata based incremental learning method for deep neural networks. IEEE Access (Vol. 7, pp. 41164–41171).
  • Meybodi, M. R., & Beigy, H. (2002a). New learning automata based algorithms for adaptation of backpropagation algorithm parameters. International Journal of Neural Systems, 12(01), 45–67. https://doi.org/10.1142/S012906570200090X
  • Meybodi, M. R., & Beigy, H. (2002b). A note on learning automata-based schemes for adaptation of BP parameters. Neurocomputing, 48(1–4), 957–974. https://doi.org/10.1016/S0925-2312(01)00686-5
  • Narendra, K. S., & Thathachar, M. A. (2012). Learning automata: An introduction. Courier corporation.
  • Rezvanian, A., Saghiri, A. M., Vahidipour, S. M., Esnaashari, M., & Meybodi, M. R. (2018). Recent advances in learning automata (Vol. 754). Springer International Publishing.
  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929–1958. http://jmlr.org/papers/v15/srivastava14a.html
  • Sudareshan, M. K., & Condarcure, T. A. (1998). Recurrent neural-network training by a learning automaton approach for trajectory learning and control system design. IEEE Transactions on Neural Networks, 9(3), 354–368. https://doi.org/10.1109/72.668879
  • Thathachar, M. A., & Sastry, P. S. (2011). Networks of learning automata: Techniques for online stochastic optimization. Springer Science and Business Media.
  • Yazidi, A., Oommen, B. J., & Goodwin, M. (2016). On solving the problem of identifying unreliable sensors without a knowledge of the ground truth: The case of stochastic environments. IEEE Transactions on Cybernetics, 47(7), 1604–1617. https://doi.org/10.1109/TCYB.2016.2552979
  • Yazidi, A., Zhang, X., Jiao, L., & Oommen, B. J. (2019). The hierarchical continuous pursuit learning automation: A novel scheme for environments with large numbers of actions. IEEE Transactions on Neural Networks and Learning Systems, 31(2), 512–526. https://doi.org/10.1109/TNNLS.2019.2905162
  • Zhang, X., Jiao, L., Oommen, B. J., & Granmo, O. C. (2019). A conclusive analysis of the finite-time behavior of the discretized pursuit learning automaton. IEEE Transactions on Neural Networks and Learning Systems, 31(1), 284–294. https://doi.org/10.1109/TNNLS.2019.2900639

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.