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Pruning Elman neural network and its application in bolt defects classification

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Abstract

The Elman model is developed based on BP neural network. Because Elman neural network has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the area of classification. The application of Elman has a bottleneck problem, that is, network structure is difficult to determine. This paper proposed an improved Elman neural network model using pruning algorithm for the network structure optimization. Firstly, we choose the bolt defect parameters which are relatively easy to detect as the classification sample, select the initial large-scale network structure, analysis the contribution of hidden nodes in the network, and reduce the dynamic threshold of hidden nodes in combination with the network to obtain the optimal network structure. Simulation results show that pruning Elman neural network (P-Elman) can adaptively obtain the optimal network structure, and achieve a high classification accuracy, which is much higher than other methods.

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References

  1. Wang RB, Xu HY, Li B, Feng Y (2017) Research on method of determining hidden layer nodes in BP neural network. Comput Technol Dev 4:1–6

    Google Scholar 

  2. Yang S, Qiao JF, Bo YC, Han HG (2013) A novel lateral inhibition neural network based on neocortex topology. Control Decis 28(11):1702–1706

    Google Scholar 

  3. Wang N, Zhang WW, Chang H (2017) Research on method of determining hidden layer nodes in BP neural network. J Taiyuan Univ Sci Technol 38(2):152–156

    Google Scholar 

  4. Zhang WD, Lv SS, Chang XS (2016) Unequalized data classification algorithm based on improved BP neural network. Appl Comput 26(6):153–156

    Google Scholar 

  5. Qiao JF, Han G, Han HG (2014) Neural network on-line modeling and controlling method for multi-variable control of wastewater treatment process. Asian J Control 16(4):1213–1223

    Article  MathSciNet  MATH  Google Scholar 

  6. Wang XZ, Dong CR (2009) Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567

    Article  Google Scholar 

  7. Shi LJ (2016) Research on nondestructive detection of bolt anchorage quality based on improved Elman neural network. Master thesis, Shijiazhuang Tiedao University, Shijiazhuang, China

  8. Bi ZM, Wang Y (2017) Shape recognition based on Elman network optimized by improved genetic algorithm. J Iron Steel Res 29(4):305–311

    Google Scholar 

  9. Ye RL, Guo ZZ, Liu RY, Liu JN (2017) Prediction of wind speed and wind power in wind farms based on wavelet packet decomposition and improved Elman neural network. Trans Chin Soc Electrotech Soc 21(32):104–109

    Google Scholar 

  10. He Y, Wang X, Huang JZ (2016) Recent advances in multiple criteria decision making techniques [J]. Int J Mach Learn Cybern 356(9232):1–4

    Google Scholar 

  11. Wang XZ, He YL, Dong LC (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci Int J 181(19):4230–4252

    MATH  Google Scholar 

  12. Hua PH, Yi CY, Xiong PY (2013) Quantum gate Elman neural network and its quantized extended gradient back-propagation training algorithm. Acta Autom Sin 39(9):1511–1522

    MathSciNet  MATH  Google Scholar 

  13. Liou Y, Huang JC, Yang WC (2008) Modeling word perception using the Elman network. Neurocomputing 71(16):3150–3157

    Article  Google Scholar 

  14. Qi WM, Cheng YC, Ji QL (2005) PID Elman neural network and its application to dynamical system identification. Control Decis 20(10):1197–1200

    MATH  Google Scholar 

  15. Song Z, Guo WY, Wang L, Xie NG (2008) Research on fault diagnosis of engine gearbox based on modified Elman neural network. Chin J Sci Instrum 29(7):1414–1417

    Google Scholar 

  16. Cui AQ, Xu H, Jia PF (2011) An Elman neural network-based model for predicting anti-germ performances and ingredient levels with limited experimental data. Expert Syst Appl 38(7):8186–8192

    Article  Google Scholar 

  17. Li ZX, Wang WL, Hu WJ, Wong KP (2010) Least squares support vector machines based predictive feedback scheduling for resource-constrained networks. Control Decis 25(3):361–366

    Google Scholar 

  18. Liu XY, Tian JY, Wang F, Bai Y (2017) An optimization of neural network structure based on gray relation-contribution deletion method. Comput Eng Des 38(3):779–783

    Google Scholar 

  19. Wu SX (2016) Application of growth pruning neural network in bolt defect identification. Master thesis, Shijiazhuang Tiedao University, Shijiazhuang, China

  20. Han HG, Qiao JF, Bo YC (2012) Structure design of RBF neural network based on information strength. J Autom 38(7):1083–1090

    MathSciNet  Google Scholar 

  21. Lv WY, Xiao Y, Xu KW, Jiang SW (2017) Electronic component performance prediction based on improved Elman neural network. Mod Def Technol 45(1):153–160

    Google Scholar 

  22. Shi XH (2006) Some theoretical studies of Elman neural networks and evolutionary algorithms and their applications. Ph.D. dissertation, JiLin University, JiLin, China

  23. Xie QG, Shen Y, Wan SS (2002) On convergence analysis of Elman neural network. Comput Eng Appl 36:65–66

    Google Scholar 

  24. Sun XY, Wu SX, Han G (2018) Application of pruning neural network in the identification of defects of bolt anchorage. Vib Shock 37(5):34–42

    Google Scholar 

  25. Karabatak M, Ince MC (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36(2):3465–3469

    Article  Google Scholar 

  26. Chen H, Yang B, Liu J, Liu D (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38(7):9014–9022

    Article  Google Scholar 

  27. Senapati MR, Mohanty AK, Dash S, Dash PK (2013) Local linear wavelet neural network for breast cancer recognition. Neural Comput Appl 22(1):125–131

    Article  Google Scholar 

  28. Dehuri K, Roy R, Cho SB (2012) An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. J Syst Softw 85(6):1333–1345

    Article  Google Scholar 

  29. King RD, Feng C (2005) Comparison of classification algorithms on large real-world problems. Appl Artif Intell 9(3):289–333

    Article  Google Scholar 

  30. Bi YL, Gan N. Wong (2016) Optimization of BP neural network based on improved genetic algorithm and its application to quality classification of red wine. Comput Meas Control 24(1):361–366

    Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China Grant no. 51674169. The authors would like to thank the Editor-in-Chief, Associate Editor and reviewers for their valuable comments which have led to significant improvement of the quality of the paper.

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  1. School of Electrical and Electronics Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, Hebei, China

    Xiaoyun Sun, Shijie Gong, Guang Han, Mingming Wang & An Jin

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  1. Xiaoyun Sun
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  2. Shijie Gong
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  3. Guang Han
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  4. Mingming Wang
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  5. An Jin
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Correspondence to Guang Han.

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Sun, X., Gong, S., Han, G. et al. Pruning Elman neural network and its application in bolt defects classification. Int. J. Mach. Learn. & Cyber. 10, 1847–1862 (2019). https://doi.org/10.1007/s13042-018-0871-0

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  • DOI: https://doi.org/10.1007/s13042-018-0871-0

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