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Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks Cover

Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks

Open Access
|Mar 2023

References

  1. Marcin Gabryel, Dawid Lada, Zbigniew Filutowicz, Zofia Patora-Wysocka, Marek Kisiel-Dorohinicki, and Guang Yi Chen. Detecting anomalies in advertising web traffic with the use of the variational autoencoder. Journal of Artificial Intelligence and Soft Computing Research, 12(4):255–256, 2022.10.2478/jaiscr-2022-0017
  2. Marcin Zalasiński, Łukasz Laskowski, Tacjana Niksa-Rynkiewicz, Krzysztof Cpałka, Alek-sander Byrski, Krzysztof Przybyszewski, Paweł Trippner, and Shi Dong. Evolutionary algorithm for selecting dynamic signatures partitioning approach. Journal of Artificial Intelligence and Soft Computing Research, 12(4):267–279, 2022.10.2478/jaiscr-2022-0018
  3. S. Albawi, T. A. Mohammed, and S. Al-Zawi. Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET), pages 1–6, 2017.10.1109/ICEngTechnol.2017.8308186
  4. A. M. Taqi, A. Awad, F. Al-Azzo, and M. Milanova. The impact of multi-optimizers and data augmentation on tensorflow convolutional neural network performance. In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pages 140–145, April 2018.10.1109/MIPR.2018.00032
  5. Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, and Tsuhan Chen. Recent advances in convolutional neural networks. Pattern Recognition, 77:354 – 377, 2018.10.1016/j.patcog.2017.10.013
  6. Robert K. Nowicki and Janusz T. Starczewski. A new method for classification of imprecise data using fuzzy rough fuzzification. Inf. Sci., 414:33–52, 2017.10.1016/j.ins.2017.05.049
  7. Janusz T. Starczewski, Katarzyna Nieszporek, Michal Wróbel, and Konrad Grzanek. A fuzzy SOM for understanding incomplete 3d faces. In ICAISC (2), volume 10842 of Lecture Notes in Computer Science, pages 73–80. Springer, 2018.10.1007/978-3-319-91262-2_7
  8. Michal Wróbel, Katarzyna Nieszporek, Janusz T. Starczewski, and Andrzej Cader. A fuzzy measure for recognition of handwritten letter strokes. In ICAISC (1), volume 10841 of Lecture Notes in Computer Science, pages 761–770. Springer, 2018.10.1007/978-3-319-91253-0_70
  9. Jarosław Bilski, Bartosz Kowalczyk, Alina Marchlewska, and Jacek M. Zurada. Local levenberg-marquardt algorithm for learning feedforwad neural networks. Journal of Artificial Intelligence and Soft Computing Research, 10(4):299–316, 2020.10.2478/jaiscr-2020-0020
  10. Jarosław Bilski, Bartosz Kowalczyk, Andrzej Marjański, Michał Gandor, and Jacek Zurada. A novel fast feedforward neural networks training algorithm. Journal of Artificial Intelligence and Soft Computing Research, 11(4):287–306, 2021.10.2478/jaiscr-2021-0017
  11. Jarosław Bilski, Bartosz Kowalczyk, Marek Kisiel-Dorohinicki, Agnieszka Siwocha, and Jacek Żurada. Towards a very fast feedforward multilayer neural networks training algorithm. Journal of Artificial Intelligence and Soft Computing Research, 12(3):181–195, 2022.10.2478/jaiscr-2022-0012
  12. Xin Wang, Yi Guo, Yuanyuan Wang, and Jinhua Yu. Automatic breast tumor detection in abvs images based on convolutional neural network and superpixel patterns. Neural Computing and Applications, 31(4):1069–1081, 2019.10.1007/s00521-017-3138-x
  13. Muhammad Irfan Sharif, Jian Ping Li, Muhammad Attique Khan, and Muhammad Asim Saleem. Active deep neural network features selection for segmentation and recognition of brain tumors using mri images. Pattern Recognition Letters, 129:181 – 189, 2020.10.1016/j.patrec.2019.11.019
  14. P. Mohamed Shakeel, T. E. E. Tobely, H. Al-Feel, G. Manogaran, and S. Baskar. Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access, 7:5577–5588, 2019.10.1109/ACCESS.2018.2883957
  15. Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, and Alexandr A. Kalinin. Deep convolutional neural networks for breast cancer histology image analysis. In Aurélio Campilho, Fakhri Karray, and Bart ter Haar Romeny, editors, Image Analysis and Recognition, pages 737–744, Cham, 2018. Springer International Publishing.10.1007/978-3-319-93000-8_83
  16. Xin Cai, Yufeng Qian, Qingshan Bai, and Wei Liu. Exploration on the financing risks of enterprise supply chain using back propagation neural network. Journal of Computational and Applied Mathematics, 367:112457, 2020.10.1016/j.cam.2019.112457
  17. Amin Hedayati Moghaddam, Moein Hedayati Moghaddam, and Morteza Esfandyari. Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41):89 – 93, 2016.10.1016/j.jefas.2016.07.002
  18. Songqiao Qi, Kaijun Jin, Baisong Li, and Yufeng Qian. The exploration of internet finance by using neural network. Journal of Computational and Applied Mathematics, 369:112630, 2020.10.1016/j.cam.2019.112630
  19. A. V. Kurbesov, D. V. Ryabkin, I. I. Miroshnichenko, N. A. Aruchidi, and K. Kh. Kalugyan. Automated voice recognition of emotions through the use of neural networks. In Rafik A. Aliev, Janusz Kacprzyk, Witold Pedrycz, Mo Jamshidi, Mustafa B. Babanli, and Fahreddin M. Sadikoglu, editors, 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019, pages 675–682, Cham, 2020. Springer International Publishing.10.1007/978-3-030-35249-3_87
  20. X. Changzhen, W. Cong, M. Weixin, and S. Yanmei. A traffic sign detection algorithm based on deep convolutional neural network. In 2016 IEEE International Conference on Signal and Image Processing (ICSIP), pages 676–679, Aug 2016.10.1109/SIPROCESS.2016.7888348
  21. Katsuba Yurii and Grigorieva Liudmila. Application of artificial neural networks in vehicles’ design self-diagnostic systems for safety reasons. Transportation Research Procedia, 20:283 – 287, 2017. 12th International Conference ”Organization and Traffic Safety Management in large cities SPbOTSIC-2016, 28-30 September 2016, St. Petersburg, Russia.10.1016/j.trpro.2017.01.024
  22. N. P. Patel and A. Kale. Optimize approach to voice recognition using iot. In 2018 International Conference On Advances in Communication and Computing Technology (ICACCT), pages 251–256, 2018.10.1109/ICACCT.2018.8529622
  23. Yi Mou and Kun Xu. The media inequality: Comparing the initial human-human and human-ai social interactions. Computers in Human Behavior, 72:432 – 440, 2017.
  24. Werbos J. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Harvard University, 1974.
  25. Scott E. Fahlman. An empirical study of learning speed in back-propagation networks. Technical report, 1988.
  26. M. Riedmiller and H. Braun. A direct adaptive method for faster backpropagation learning: the rprop algorithm. In IEEE International Conference on Neural Networks, pages 586–591 vol.1, March 1993.
  27. Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, ICML’13, pages III– 1139–III–1147. JMLR.org, 2013.
  28. M. T. Hagan and M.B. Menhaj. Training feedforward networks with the marquardt algorithm. IEEE Transactions on Neuralnetworks, 5:989–993, 1994.10.1109/72.32969718267874
  29. N. Ampazis and S. J. Perantonis. Two highly efficient second-order algorithms for training feedforward networks. IEEE Transactions on Neural Networks, 13(5):1064–1074, 2002.10.1109/TNN.2002.103193918244504
  30. J. S. Smith, B. Wu, and B. M. Wilamowski. Neural network training with Levenberg–Marquardt and adaptable weight compression. IEEE Transactions on Neural Networks and Learning Systems, 30(2):580–587, 2019.10.1109/TNNLS.2018.284677529994621
  31. Miao Cui, Kai Yang, Xiao liang Xu, Sheng dong Wang, and Xiao wei Gao. A modified Levenberg–Marquardt algorithm for simultaneous estimation of multi-parameters of boundary heat flux by solving transient nonlinear inverse heat conduction problems. International Journal of Heat and Mass Transfer, 97:908 – 916, 2016.10.1016/j.ijheatmasstransfer.2016.02.085
  32. Jiyang Dong, Ke Lu, Jian Xue, Shuangfeng Dai, Rui Zhai, and Weiguo Pan. Accelerated nonrigid image registration using improved Levenberg–Marquardt method. Information Sciences, 423:66 – 79, 2018.10.1016/j.ins.2017.09.059
  33. Jarosław Bilski, Bartosz Kowalczyk, and Jacek M. Żurada. Application of the givens rotations in the neural network learning algorithm. In Artificial Intelligence and Soft Computing, volume 9602 of Lecture Notes in Artificial Intelligence, pages 46–56. Springer-Verlag Berlin Heidelberg, 2016.10.1007/978-3-319-39378-0_5
  34. Jacek Smoląg, Jarosław Bilski, and Leszek Rutkowski. Systolic array for neural networks. In IV KSNiIZ, pages 487–497, 1999.
  35. Jacek Smoląg and Jarosław Bilski. A systolic array for fast learning of neural networks. In V NNSC, pages 754–758, 2000.
  36. D. Rutkowska, R.K. Nowicki, and Y. Hayashi. Parallel processing by implication-based neuro–fuzzy systems. Lecture Notes in Computer Science, 2328:599–607, 2002.10.1007/3-540-48086-2_66
  37. Jarosław Bilski and Jacek Smoląg. Parallel realisation of the recurrent RTRN neural network learning. In Artificial Intelligence and Soft Computing, volume 5097 of Lecture Notes in Computer Science, pages 11–16. Springer-Verlag Berlin Heidelberg, 2008.10.1007/978-3-540-69731-2_2
  38. Jarosław Bilski and Jacek Smoląg. Parallel architectures for learning the RTRN and Elman dynamic neural network. IEEE Transactions on Parallel and Distributed Systems, 26(9):2561 – 2570, 2015.10.1109/TPDS.2014.2357019
  39. Jarosław Bilski, Jacek Smoląg, and Jacek M. Żurada. Parallel approach to the Levenberg-Marquardt learning algorithm for feedforward neural networks. In Artificial Intelligence and Soft Computing, volume 9119 of Lecture Notes in Computer Science, pages 3–14. Springer-Verlag Berlin Heidelberg, 2015.10.1007/978-3-319-19324-3_1
  40. J. Bilski and B.M. Wilamowski. Parallel Levenberg-Marquardt algorithm without error backpropagation. Artificial Intelligence and Soft Computing, Springer-Verlag Berlin Heidelberg, LNAI 10245:25–39, 2017.10.1007/978-3-319-59063-9_3
Language: English
Page range: 45 - 61
Submitted on: Oct 19, 2022
Accepted on: Feb 15, 2023
Published on: Mar 11, 2023
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Jarosław Bilski, Jacek Smoląg, Bartosz Kowalczyk, Konrad Grzanek, Ivan Izonin, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.