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Automatic fault diagnosis of computer networks based on a combination BP neural network and fuzzy logic | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 15، دوره 15، شماره 5، مرداد 2024، صفحه 177-187 اصل مقاله (916.16 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.29622.4208 | ||
| نویسندگان | ||
| Elham Bideh1؛ Mohammadreza Fadavi Amiri1؛ Javad Vahidi* 2؛ Majid Iranmanesh3 | ||
| 1Department of Computer Engineering, Shomal University, Amol, Iran | ||
| 2Department of Computer Science, Iran University of Science and Technology, Tehran, Iran | ||
| 3Department of Mathematics, Semnan University, Semnan, Iran | ||
| تاریخ دریافت: 15 مرداد 1401، تاریخ بازنگری: 31 شهریور 1401، تاریخ پذیرش: 08 مهر 1401 | ||
| چکیده | ||
| Today, computer network fault diagnosis is one of the key challenges experts are facing in the field of computer networks. Therefore, achieving an automatic diagnosis system which is based on artificial intelligence methods and is able to diagnose faults with maximum accuracy and speed is of high importance. One of the methods which is studied and utilized up to now is artificial neural networks with a back propagation algorithm while using neural networks with a back propagation algorithm has two main challenges in front. The first challenge is related to the backpropagation learning type as it is a supervised learning requiring inductive knowledge driven from previous conditions. The second challenge is the long time required for training such a neural network. In this work, combining neural networks with a backpropagation algorithm and fuzzy logic is applied as a method for confronting these challenges. The result of this study shows that fuzzy clustering is able to provide the inductive knowledge required for backpropagation learning by determining the membership degree of training samples to different clusters of network faults. Also, according to the simulations taken place, implementing a fuzzy controller in determining the learning rate in each backpropagation iteration has resulted in successful outcomes. Thus, the learning speed of this algorithm has been increased in comparison to the constant learning rate mode resulted in reducing the training time duration of this neural networks. | ||
| کلیدواژهها | ||
| Computer Networks Fault Diagnosis؛ Artificial Neural Networks؛ Back Propagation Algorithm؛ Fuzzy Clustering؛ Fuzzy Controller | ||
| مراجع | ||
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[1] F. Ahmad, Z. Ahmad, C.A. Kerrache, F. Kurugollu, A. Adnane, and E. Barka, Blockchain in internet-of-things: Architecture, applications and research directions, Int. Conf. Comput. Inf. Sci., 2019, 6 Pages. [2] M. Al-Kasassbeh, G. Al-Naymat, and E. Al-Hawari, Towards generating realistic SNMP-MIB dataset for network anomaly detection, Int. J. Comput. Sci. Inf. Secur. 14 (2016), no. 9. [3] A. Bagherinia, B. Minaei-Bidgoli, M. Hossinzadeh, and H. Parvin, Elite fuzzy clustering ensemble based on clustering diversity and quality measures, Appl. Intell.49 (2019), 1724–1747. [4] Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, Greedy layer-wise training of deep networks, 19th Int. Conf. Neural Inf. Process. Syst., December, 2006, 8 Pages. [5] J.C. Bezdek, R. Ehrlich, and W. Full, FCM: The fuzzy c-means clustering algorithm, Comput. Geosci. 10 (1984), no. 2-3, 191–203. [6] J.C. Bezdek, R.J. Hathaway, M.J. Sabin, and W.T. Tucker, Convergence theory for fuzzy c-means: Counterexamples and repairs, IEEE Trans. Syst. Man Cybernet. SMC-17 (1987), no. 5. [7] M.C. Choy, D. Srinivasan, and R.L. Cheu, Neural networks for continuous online learning and control, IEEE Trans. Neural Networks 17 (2006), no. 6, 1511–1531. [8] M. Georgiopoulos, C. Li, and T. Kocak, Learning in the feed-forward random neural network: A critical review, Perform. Eval. 68 (2011), no. 4, 361–384. [9] N. Goyal, M. Dave, and A.K. Verma, Fuzzy based clustering and aggregation technique for under water wireless sensor networks, Int. Conf. Electron. Commun. Syst., 2014, 5 Pages. [10] C-M. Hsu, Forecasting stock/futures prices by using neural networks with feature selection, 6th IEEE Joint Int. Inf. Technol. Artific. Intell. Conf., August 2011, 7 Pages. [11] Q. Hu, and D.B. Hertz, Fuzzy logic controlled neural network learning, Inf. Sci. 2 (1994), no. 1, 15–33. [12] J.P. Jesan, The Neural Approach to Pattern Recognition, ACM Digital Library, April 2004, https://ubiquity.acm.org/article.cfm?id=985625. [13] T. Kaur, Implementation of backpropagation algorithm: A neural network approach for pattern recognition, Int. J. Engin. Res. Dev. 1 (2012), no. 5, 30–37. [14] A.S. Khan, Z. Ahmad, J. Abdullah, and F. Ahmad, A spectrogram image-based network anomaly detection system using deep convolutional neural network, IEEE Access 9 (2021), 87079–87093. [15] S.Y. Kung, and J.N. Hwang, An algebraic projection analysis for optimal hidden units size and learning rates in back-propagation learning, IEEE Int. Conf. Neural Networks, 1988, 8 Pages. [16] C.Y. Lee, M.S. Wen, G.L. Zhou, and T.A. Le, Application of ANN in induction-motor fault-detection system established with MRA and CFFS, Mathematics 10 (2022), no. 13, 2250. [17] M. Madhiarasan and S.N. Deepa, A Novel Criterion to Select Hidden Neuron Numbers in Improved Back Propagation Networks for Wind Speed Forecasting, Appl. Intell. 44 (2016), 878–893. [18] K. Qader and M. Adda, Network faults classification using FCM, 17th Int. Conf. Distributed Comput. Commun. Networks (DCCN), 2013, 8 Pages. [19] M. Qingwu, L. Chengbin, C. Hu, and L. Ti, Improved BP network algorithm based on fuzzy logic and application in geophysics, Eur. Symp. Intell. Techniq., September, 2000, 8 Pages. [20] H. Rashidy Kanan, and M. Yousefi Azar Khanian, Reduction of neural network training time using an adaptive fuzzy approach in real-time applications, Int. J. Inf. Electron. Engin. 2 (2012), no. 3. [21] P.D. Raval, and A.S. Pandya, Improved fault classification in series compensated EHV transmission line using wavelet transform and artificial neural network, IEEE 1st Int. Conf. Power Electron. Intell. Control Energy Syst., July, 2016, 5 Pages. [22] D. Srinivasan, X. Jin, and R.L. Cheu, Adaptive neural network models for automatic incident detection on freeways, Neurocomputing 64 (2005), 473–496. [23] S.N. Sivanandam, S. Sumathi, and S.N. Deepa, Introduction to Neural Networks using Matlab 6.0, 1st ed., Tata McGraw Hill, India, 2006. [24] C. Van Kien, H.P.H. Anh, and N.N. Son, Adaptive inverse multilayer fuzzy control for uncertain nonlinear system optimizing with differential devolution algorithm, Appl. Intell. 51 (2021), 527–548. [25] R. Wan, N. Xiong, Q. Hu, H.Wang, and J. Shang, Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks, EURASIP J. Wireless Commun. Network. 2019 (2019), 1–11. [26] Q. Wang, Computer network fault diagnosis based on neural network, Int. J. Future Gener. Commun. Network. 8 (2015), no. 5, 39–50. [27] J. Zhang, Modelling and optimal control of batch processes using recurrent neuro-fuzzy networks, IEEE Trans. Fuzzy Syst. 13 (2005), no. 4, 417–427. | ||
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