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A Novel Approach to the GQR Algorithm for Neural Networks Training

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Artificial Intelligence and Soft Computing (ICAISC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14125))

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Abstract

In this paper, a novel approach to the GQR algorithm is presented. The idea revolves around batch training for the feedforward neural networks. The core of this paper contains a mathematical explanation for the batch approach, which can be utilized in the GQR algorithm. The final section of the article contains several simulations. They prove the novel approach to be superior to the original GQR algorithm.

This work has been supported by the Polish National Science Center under Grant 2017/27/B/ST6/02852.

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References

  1. Bilski, J., Kowalczyk, B., Marchlewska, A., Żurada, J.M.: Local Levenberg-Marquardt algorithm for learning feedforwad neural networks. J. Artif. Intell. Soft Comput. Res. 10(4), 299–316 (2020)

    Article  Google Scholar 

  2. Bilski, J., Rutkowski, L.: A fast training algorithm for neural networks. IEEE Trans. Circ. Syst. Part II 45(6), 749–753 (1998)

    Google Scholar 

  3. Bilski, J., Smolag, J.: Parallel architectures for learning the RTRN and Elman dynamic neural network. IEEE Trans. Parallel Distrib. Syst. 26(9), 2561–2570 (2015)

    Article  Google Scholar 

  4. Bilski, J., Wilamowski, B.M.: Parallel Levenberg-Marquardt algorithm without error backpropagation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 25–39. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_3

    Chapter  Google Scholar 

  5. Bilski, J., Kowalczyk, B., Kisiel-Dorohinicki, M., Siwocha, A., Żurada, J.: Towards a very fast feedforward multilayer neural networks training algorithm (2022)

    Google Scholar 

  6. Bilski, J., Kowalczyk, B., Marjanski, A., Gandor, M., Żurada, J.: A novel fast feedforward neural networks training algorithm. J. Artif. Intell. Soft Comput. Res. 11(4), 287–306 (2021)

    Article  Google Scholar 

  7. Bilski, J., Rutkowski, L., Smoląg, J., Tao, D.: A novel method for speed training acceleration of recurrent neural networks. Inf. Sci. 553, 266–279 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  8. Bilski, J., Smoląg, J., Kowalczyk, B., Grzanek, K., Izonin, I.: Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks. J. Artif. Intell. Soft Comput. Res. 12(2), 45–61 (2023)

    Article  Google Scholar 

  9. Bougueroua, N., Mazouzi, S., Belaoued, M., Seddari, N., Derhab, A., Bouras, A.: A survey on multi-agent based collaborative intrusion detection systems. J. Artif. Intell. Soft Comput. Res. 11(2), 111–142 (2021)

    Article  Google Scholar 

  10. Cierniak, R., et al.: A new statistical reconstruction method for the computed tomography using an x-ray tube with flying focal spot. J. Artif. Intell. Soft Comput. Res. 11(4), 243–266 (2021)

    Article  Google Scholar 

  11. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)

    MathSciNet  MATH  Google Scholar 

  12. Gabryel, M., Grzanek, K., Hayashi, Y.: Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic. J. Artif. Intell. Soft Comput. Res. 10(4), 243–253 (2020)

    Article  Google Scholar 

  13. Gabryel, M., Lada, D., Filutowicz, Z., Patora-Wysocka, Z., Kisiel-Dorohinicki, M., Chen, G.Y.: Detecting anomalies in advertising web traffic with the use of the variational autoencoder. J. Artif. Intell. Soft Comput. Res. 12(4), 255–256 (2022)

    Article  Google Scholar 

  14. Hagan, M.T., Menhaj, M.B.: Training feedforward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 5, 989–993 (1994)

    Article  Google Scholar 

  15. Hinton, G., Sejnowski, T.J.: Unsupervised Learning: Foundations of Neural Computation. The MIT Press, Cambridge (1999)

    Google Scholar 

  16. Kiełbasiński, A., Schwetlick, H.: Numeryczna Algebra Liniowa: Wprowadzenie do Obliczeń Zautomatyzowanych. Wydawnictwa Naukowo-Techniczne, Warszawa (1992)

    Google Scholar 

  17. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)

    Google Scholar 

  18. Niksa-Rynkiewicz, T., Szewczuk-Krypa, N., Witkowska, A., Cpalka, K., Zalasinski, M., Cader, A.: Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network. J. Artif. Intell. Soft Comput. Res. 11(2), 143–155 (2021)

    Article  Google Scholar 

  19. Pérez-Pons, M.E., Parra-Dominguez, J., Omatu, S., Herrera-Viedma, E., Corchado, J.M.: Machine learning and traditional econometric models: a systematic mapping study. J. Artif. Intell. Soft Comput. Res. 12(2), 79–100 (2022)

    Article  Google Scholar 

  20. Werbos, J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Harvard University (1974)

    Google Scholar 

  21. Woldan, P., Duda, P., Cader, A., Laktionov, I.: A new approach to image-based recommender systems with the application of heatmaps maps. J. Artif. Intell. Soft Comput. Res. 12(2), 63–72 (2023)

    Article  Google Scholar 

  22. Zalasinski, M., et al.: Evolutionary algorithm for selecting dynamic signatures partitioning approach (2022)

    Google Scholar 

  23. Zeiler, M.: ADADELTA: an adaptive learning rate method (2012)

    Google Scholar 

  24. Zhao, X., Song, M., Liu, A., Wang, Y., Wang, T., Cao, J.: Data-driven temporal-spatial model for the prediction of AQI in Nanjing. J. Artif. Intell. Soft Comput. Res. 10(4), 255–270 (2020)

    Article  Google Scholar 

  25. El Zini, J., Rizk, Y., Awad, M.: An optimized parallel implementation of non-iteratively trained recurrent neural networks. J. Artif. Intell. Soft Comput. Res. 11(1), 33–50 (2021)

    Article  Google Scholar 

  26. Żurada, J.M.: Introduction to Artificial Neural Systems. West (1992)

    Google Scholar 

  27. Łapa, K., Cpałka, K., Kisiel-Dorohinicki, M., Paszkowski, J., Dębski, M., Le, V.-H.: Multi-population-based algorithm with an exchange of training plans based on population evaluation (2022)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computational Intelligence, Czȩstochowa University of Technology, Czȩstochowa, Poland

    Jarosław Bilski & Bartosz Kowalczyk

Authors
  1. Jarosław Bilski
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  2. Bartosz Kowalczyk
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Corresponding author

Correspondence to Jarosław Bilski .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Bilski, J., Kowalczyk, B. (2023). A Novel Approach to the GQR Algorithm for Neural Networks Training. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science(), vol 14125. Springer, Cham. https://doi.org/10.1007/978-3-031-42505-9_1

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  • DOI: https://doi.org/10.1007/978-3-031-42505-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42504-2

  • Online ISBN: 978-3-031-42505-9

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