18
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A novel approach for BOA trained ANN for channel equalization problems

, , &
Pages 2121-2130 | Received 01 Sep 2022, Published online: 16 Dec 2022

References

  • J.C. Patra, Wei Beng Poh, N.S. Chaudhari, and A. Das, “Nonlinear channel equalization with QAM signal using Chebyshev artificial neural network.,” In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. pp. 3214–3219. IEEE.
  • H. Zhao, X. Zeng, J. Zhang, Y. Liu, X. Wang, and T. Li, “A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.,” Neural Networks. vol. 24, no. 1, pp. 12–18, 2011. doi: 10.1016/j.neunet.2010.09.009
  • H. Zhao, X. Zeng, X. Zhang, J. Zhang, Y. Liu, and T. Wei, “An adaptive decision feedback equalizer based on the combination of the FIR and FLNN.,” Digital Signal Processing. vol. 21, no. 6, pp. 679–689, 2011. doi: 10.1016/j.dsp.2011.05.004
  • B. Kumar, V.P. Singh, and Y.K. Chauhan, “Fuzzy logic based adaptation mechanism for MRAS based speed estimators.,” Journal of Information and Optimization Sciences. vol. 40, no. 2, pp. 455–466, 2019. doi: 10.1080/02522667.2019.1580885
  • P. Mohapatra, T. Samantara, S.P. Panigrahi, and S.K. Nayak, “Equalization of communication channels using GA-trained RBF networks.,” In: Advances in Intelligent Systems and Computing. pp. 491–499 (2018).
  • R. Martinek and J. Zidek, “The Real Implementation of ANFIS Channel Equalizer on the System of Software-Defined Radio.,” IETE Journal of Research. vol. 60, no. 2, pp. 183–193, 2014. doi: 10.1080/03772063.2014.914698
  • P. Priyadarshi and C.S. Rai, “Rprop and improved Rprop+ based constant modulus type (RCMT) blind channel equalization algorithm for QAM signal.,” Journal of Information and Optimization Sciences. vol. 40, no. 2, pp. 351–366, 2019. doi: 10.1080/02522667.2019.1586351
  • G. Panda, J.K. Satpathy, and S.K. Patra, “Development of New Neural Adaptive Equalisers and their Performance Comparison with Existing Techniques.,” IETE Journal of Research. vol. 42, no. 4–5, pp. 237–254, 1996. doi: 10.1080/03772063.1996.11415930
  • O. Sahu and S. Kumar, “A New Channel Equalizer Using Adaptive Neuro Fuzzy Inference System.,” IETE Journal of Research. vol. 57, no. 3, p. 201, 2011. doi: 10.4103/0377-2063.83640
  • S.R. Palla, G. Sahu, and P. Parida, “Human gait recognition using firefly template segmentation.,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. pp. 1–11, 2021.
  • R. Gupta and S.K. Malik, “A classification using RDFLIB and SPARQL on RDF dataset.,” Journal of Information and Optimization Sciences. vol. 43, no. 1, pp. 143–154, 2022. doi: 10.1080/02522667.2022.2039461
  • A.Y.H. Liao, J.-I. Shieh, H.-H. Wu, and S.-Y. Lin, “A case study of using the generalized K-harmonic means method in decision-making processes.,” Journal of Information and Optimization Sciences. vol. 29, no. 5, pp. 813–834, 2008. doi: 10.1080/02522667.2008.10699838
  • A.S. Mohanty, P. Parida, and K.C. Patra, “Usage of ML Techniques for ASD Detection.,” In: Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications. pp. 91–112. CRC Press, Boca Raton (2022).
  • Kumar Mohapatra, Pradyumna, Ravi Narayan Panda, Saroja Kumar Rout, Rojalin Samantaroy, and Pradeep Kumar Jena. “A Novel Application of HPSOGWO Trained ANN in Nonlinear Channel Equalization.” In Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering, pp. 159-174. Springer, Singapore, 2023.
  • Mohapatra, P. K., Panda, R. N., Kumar Rout, S., Samantaroy, R., & Jena, P. K. (2023). A Novel Cuckoo Search Optimized RBF Trained ANN in a Nonlinear Channel Equalization. In Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering (pp. 189-203). Springer, Singapore.
  • Mohapatra, Pradyumna Kumar, et al. “Training Strategy of Fuzzy-Firefly based ANN in Non-linear Channel Equalization.” IEEE Access (2022).
  • Arora, Sankalap, and Satvir Singh. “Butterfly optimization algorithm: a novel approach for global optimization.” Soft Computing 23.3 (2019): 715-734. doi: 10.1007/s00500-018-3102-4

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.