1. bookVolumen 11 (2021): Heft 4 (October 2021)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2449-6499
Erstveröffentlichung
30 Dec 2014
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
Uneingeschränkter Zugang

A Novel Fast Feedforward Neural Networks Training Algorithm

Online veröffentlicht: 08 Oct 2021
Volumen & Heft: Volumen 11 (2021) - Heft 4 (October 2021)
Seitenbereich: 287 - 306
Eingereicht: 15 Feb 2021
Akzeptiert: 24 Jul 2021
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2449-6499
Erstveröffentlichung
30 Dec 2014
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

[1] J. Werbos. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Harvard University, 1974. Search in Google Scholar

[2] J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, and T. Chen. Recent advances in convolutional neural networks. Pattern Recognition, 77: 354–377, 2018.10.1016/j.patcog.2017.10.013 Search in Google Scholar

[3] J. Bilski and A.I. Galushkin. A new proposition of the activation function for significant improvement of neural networks performance. In Artificial Intelligence and Soft Computing, volume 9602 of Lecture Notes in Computer Science, pages 35–45. Springer-Verlag Berlin Heidelberg, 2016.10.1007/978-3-319-39378-0_4 Search in Google Scholar

[4] N.A. Khan and A. Shaikh. A smart amalgamation of spectral neural algorithm for nonlinear lane-emden equations with simulated annealing. Journal of Artificial Intelligence and Soft Computing Research, 7(3): 215–224, 2017.10.1515/jaiscr-2017-0015 Search in Google Scholar

[5] O. Chang, P. Constante, A. Gordon, and M. Singana. A novel deep neural network that uses space-time features for tracking and recognizing a moving object. Journal of Artificial Intelligence and Soft Computing Research, 7(2): 125–136, 2017.10.1515/jaiscr-2017-0009 Search in Google Scholar

[6] A. Shewalkar, D. Nyavanandi, and S. A. Ludwig. Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. Journal of Artificial Intelligence and Soft Computing Research, 9(4): 235–245, 2019. Search in Google Scholar

[7] J.B. Liu, J. Zhao, S. Wang, M. Javaid, and J. Cao. On the topological properties of the certain neural networks. Journal of Artificial Intelligence and Soft Computing Research, 8(4): 257–268, 2018.10.1515/jaiscr-2018-0016 Search in Google Scholar

[8] Y. Li, R. Cui, Z. Li, and D. Xu. Neural network approximation based near-optimal motion planning with kinodynamic constraints using rrt. IEEE Transactions on Industrial Electronics, 65(11): 8718–8729, Nov 2018.10.1109/TIE.2018.2816000 Search in Google Scholar

[9] R. Shirin. A neural network approach for retailer risk assessment in the aftermarket industry. Benchmarking: An International Journal, 26(5): 1631–1647, Jan 2019.10.1108/BIJ-06-2018-0162 Search in Google Scholar

[10] M. Costam, D. Oliveira, S. Pinto, and A. Tavares. Detecting driver’s fatigue, distraction and activity using a non-intrusive ai-based monitoring system. Journal of Artificial Intelligence and Soft Computing Research, 9(4): 247–266, 2019.10.2478/jaiscr-2019-0007 Search in Google Scholar

[11] A.K. Singh, S.K. Jha, and A.V. Muley. Candidates selection using artificial neural network technique in a pharmaceutical industry. In Siddhartha Bhattacharyya, Aboul Ella Hassanien, Deepak Gupta, Ashish Khanna, and Indrajit Pan, editors, International Conference on Innovative Computing and Communications, pages 359–366, Singapore, 2019. Springer Singapore.10.1007/978-981-13-2354-6_38 Search in Google Scholar

[12] A.Y. Hannun, P. Rajpurkar, M. Haghpanahi, G.H. Tison, C. Bourn, M. P. Turakhia, and A.Y. Ng. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1): 65–69, 2019.10.1038/s41591-018-0268-3678483930617320 Search in Google Scholar

[13] D. Hagan and H. Hagan. Soft computing tools for virtual drug discovery. Journal of Artificial Intelligence and Soft Computing Research, 8(3): 173–189, 2018.10.1515/jaiscr-2018-0012 Search in Google Scholar

[14] E. Angelini, G. di Tollo, and A. Roli. A neural network approach for credit risk evaluation. The Quarterly Review of Economics and Finance, 48(4): 733–755, 2008.10.1016/j.qref.2007.04.001 Search in Google Scholar

[15] Ghosh and Reilly. Credit card fraud detection with a neural-network. In 1994 Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences, volume 3, pages 621–630, Jan 1994.10.1109/HICSS.1994.323314 Search in Google Scholar

[16] K.Y. Tam and M. Kiang. Predicting bank failures: A neural network approach. Applied Artificial Intelligence, 4(4): 265–282, 1990. Search in Google Scholar

[17] U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, and H. Adeli. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine, 100: 270–278, 2018.10.1016/j.compbiomed.2017.09.01728974302 Search in Google Scholar

[18] O. Abedinia, N. Amjady, and N. Ghadimi. Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Computational Intelligence, 34(1): 241–260, 2018.10.1111/coin.12145 Search in Google Scholar

[19] H. Liu, X. Mi, and Y. Li. Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network. Energy Conversion and Management, 156: 498–514, 2018.10.1016/j.enconman.2017.11.053 Search in Google Scholar

[20] J.C.R. Whittington and R. Bogacz. Theories of error back-propagation in the brain. Trends in Cognitive Sciences, 23(3): 235–250, 2019.10.1016/j.tics.2018.12.005638246030704969 Search in Google Scholar

[21] A.K. Singh, B. Kumar, S.K. Singh, S.P. Ghrera, and A. Mohan. Multiple watermarking technique for securing online social network contents using back propagation neural network. Future Generation Computer Systems, 86: 926–939, 2018.10.1016/j.future.2016.11.023 Search in Google Scholar

[22] Z. Cao, N. Guo, M. Li, K. Yu, and K. Gao. Back propagation neural network based signal acquisition for Brillouin distributed optical fiber sensors. Opt. Express, 27(4): 4549–4561, Feb 2019.10.1364/OE.27.00454930876072 Search in Google Scholar

[23] M.T. Hagan and M.B. Menhaj. Training feed-forward networks with the marquardt algorithm. IEEE Transactions on Neuralnetworks, 5: 989–993, 1994.10.1109/72.32969718267874 Search in Google Scholar

[24] B.T. Polyak. Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics, 4(5): 1–17, 1964.10.1016/0041-5553(64)90137-5 Search in Google Scholar

[25] Yu. E. Nesterov. A method for solving the convex programming problem with convergence rate O(1/sqr(k)). In Soviet Mathematics Dok-lady, number 27: 372-376, 1983. Search in Google Scholar

[26] I. Sutskever, J. Martens, G. Dahl, and G. 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. Search in Google Scholar

[27] S.E. Fahlman. An empirical study of learning speed in back-propagation networks. Technical report, 1988. Search in Google Scholar

[28] 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. Search in Google Scholar

[29] D.P. Kingma and J. Ba. Adam: A method for stochastic optimization, 2014. Search in Google Scholar

[30] J. Bilski and L. Rutkowski. A fast training algorithm for neural networks. IEEE Transaction on Circuits and Systems Part II, 45(6): 749–753, 1998.10.1109/82.686696 Search in Google Scholar

[31] W. Givens. Computation of plain unitary rotations transforming a general matrix to triangular form. Journal of The Society for Industrial and Applied Mathematics, 6: 26–50, 1958.10.1137/0106004 Search in Google Scholar

[32] C.L. Lawson and R.J. Hanson. Solving Least Squares Problems. Prentice-Hall series in automatic computation. Prentice-Hall, 1974. Search in Google Scholar

[33] A. Kiełbasiński and H. Schwetlick. Numeryczna Algebra Liniowa: Wprowadzenie do Obliczeń Zautomatyzowanych. Wydawnictwa Naukowo-Techniczne, Warszawa, 1992. Search in Google Scholar

[34] Louis Guttman. Enlargement Methods for Computing the Inverse Matrix. The Annals of Mathematical Statistics, 17(3): 336 – 343, 1946.10.1214/aoms/1177730946 Search in Google Scholar

[35] J. Bilski and B.M. Wilamowski. Parallel learning of feedforward neural networks without error backpropagation. In Artificial Intelligence and Soft Computing, pages 57–69, Cham, 2016. Springer International Publishing.10.1007/978-3-319-39378-0_6 Search in Google Scholar

[36] J. Bilski, B. Kowalczyk, and K. Grzanek. The parallel modification to the Levenberg-Marquardt algorithm. In Artificial Intelligence and Soft Computing, volume 10841 of Lecture Notes in Artificial Intelligence, pages 15–24. Springer-Verlag Berlin Heidelberg, 2018.10.1007/978-3-319-91253-0_2 Search in Google Scholar

[37] 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 Search in Google Scholar

[38] J. Bilski and J. Smoląg. Fast conjugate gradient algorithm for feedforward neural networks. In Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, and Jacek M. Zurada, editors, Artificial Intelligence and Soft Computing, pages 27–38, Cham, 2020. Springer International Publishing.10.1007/978-3-030-61401-0_3 Search in Google Scholar

Empfohlene Artikel von Trend MD

Planen Sie Ihre Fernkonferenz mit Scienceendo