Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Tuning extreme learning machine by an improved electromagnetism-like mechanism algorithm for classification problem

College of Engineering, Huazhong Agricultural University, Wuhan, Hubei, 430070, China

Special Issues: Optimization methods in Intelligent Manufacturing

Extreme learning machine (ELM) is a kind of learning algorithm for single hidden-layer feedforward neural network (SLFN). Compared with traditional gradient-based neural network learning algorithms, ELM has the advantages of fast learning speed, good generalization performance and easy implementation. But due to the random determination of input weights and hidden biases, ELM demands more hidden neurons and cannot guarantee the optimal network structure. Here, we report a new learning algorithm to overcome the disadvantages of ELM by tuning the input weights and hidden biases through an improved electromagnetism-like mechanism (EM) algorithm called DAEM and Moore-Penrose (MP) generalized inverse to analytically determine the output weights of ELM. In DAEM, three different solution updating strategies inspired by dragonfly algorithm (DA) are implemented. Experimental results indicate that the proposed algorithm DAEM-ELM has better generalization performance than traditional ELM and other evolutionary ELMs.
  Figure/Table
  Supplementary
  Article Metrics

Keywords extreme learning machine; electromagnetism-like mechanism; dragonfly algorithm; classification problem

Citation: Mengya Zhang, Qing Wu, Zezhou Xu. Tuning extreme learning machine by an improved electromagnetism-like mechanism algorithm for classification problem. Mathematical Biosciences and Engineering, 2019, 16(5): 4692-4707. doi: 10.3934/mbe.2019235

References

  • 1. W. Cao, X. Wang, Z. Ming, et al., A review on neural networks with random weights, Neurocomputing, (2017), S0925231217314613.
  • 2. G. Camps-Valls, D. Tuia, L. Bruzzone, et al., Advances in hyperspectral image classification: earth monitoring with statistical learning methods, IEEE Signal Proc. Mag., 31 (2013), 45–54.
  • 3. L. Wang, Y. Zeng and T. Chen, Back propagation neural network with adaptive differential evolution algorithm for time series forecasting, Expert Syst. Appl., 42 (2015), 855–863.
  • 4. E. Maggiori, Y. Tarabalka, G. Charpiat, et al., Convolutional neural networks for large-scale remote sensing image classification, IEEE T. Geosci. Remote, 55 (2016), 645–657.
  • 5. G. B. Huang, Q. Y. Zhu and C. K. Siew, Extreme learning machine: theory and applications, Neurocomputing, 70 (2006), 489–501.
  • 6. J. Zhang, Y. F. Lu, B. Q. Zhang, et al., Device-free localization using empirical wavelet transform-based extreme learning machine, Proceedings of the 30th Chinese Control and Decision Conference, (2018), 2585–2590.
  • 7. Y. J. Li, S. Zhang, Y. X. Yin, et al., A soft sensing scheme of gas utilization prediction for blast furnace via improved extreme learning machine, Neural Process. Lett. (2018), 10.1007/s11063-018-9888-3.
  • 8. J. Zhang, Y. F. Xu, J. Q. Xue, et al., Real-time prediction of solar radiation based on online sequential extreme learning machine, Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, (2018), 53–57.
  • 9. R. Z. Song, W. D. Xiao, Q. L. Wei, et al., Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems, Soft Comput., 18 (2014), 1645–1653.
  • 10. J. Zhang, W. D. Xiao, Y. J. Li, et al., Multilayer probability extreme learning machine for device-free localization. Neurocomputing, (2019), 10.1016/j.neucom.2018.11.106.
  • 11. Y. Park, and H. S. Yang, Convolutional neural network based on an extreme learning machine for image classification, Neurocomputing, 339 (2019), 66–76.
  • 12. G. B. Huang, H. Zhou, X. Ding, et al., Extreme learning machine for regression and multiclass classification, IEEE T. Syst. Man Cy. B., 42 (2012), 513–529.
  • 13. F. Han, H. F. Yao and Q. H. Ling, An improved evolutionary extreme learning machine based on particle swarm optimization, Neurocomputing, 116 (2013), 87–93.
  • 14. A. Rashno, B. Nazari, S. Sadri, et al., Effective pixel classification of mars images based on ant colony optimization feature selection and extreme learning machine, Neurocomputing, 226 (2017), 66–79.
  • 15. G. Li, P. Niu, Y. Ma, et al., Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency, Knowl-Based Syst., 67 (2014), 278–289.
  • 16. İ. B. Ş, and S. Fang, An electromagnetism-like mechanism for global optimization, J. Global Optim., 25 (2003), 263–282.
  • 17. C. J. Zhang, X. Y. Li, L. Gao, et al., An improved electromagnetism-like mechanism algorithm for constrained optimization, Expert Syst. Appl., 40 (2013), 5621–5634.
  • 18. C. T. Tseng, C. H. Lee, Y. S. P. Chiu, et al., A discrete electromagnetism-like mechanism for parallel machine scheduling under a grade of service provision, Int. J. Prod. Res., 55 (2017), 3149–3163.
  • 19. X. Y. Li, L. Gao, Q. K. Pan, et al., An effective hybrid genetic algorithm and variable neighborhood search for integrated process planning and scheduling in a packaging machine workshop, IEEE T. Syst. Man Cy. Syst., (2018), 10.1109/TSMC.2018.2881686.
  • 20. X. Y. Li, C. Lu, L. Gao, et al., An Effective Multi-Objective Algorithm for Energy Efficient Scheduling in a Real-Life Welding Shop, IEEE T. Ind. Inform., 14 (2018), 5400–5409.
  • 21. X. Y. Li, S. Q. Xiao, C. Y. Wang, et al., Mathematical Modeling and a Discrete Artificial Bee Colony Algorithm for the Welding Shop Scheduling Problem, Memetic Comp., (2019), 10.1007/s12293-019-00283-4.
  • 22. Q. Wu, L. Gao, X. Y. Li, et al., Applying an electromagnetism-like mechanism algorithm on parameter optimisation of a multi-pass milling process, Int. J. Prod. Res., 51 (2013), 1777–1788.
  • 23. K. J. Wang, A. M. Adrian, K. H. Chen, et al., An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus, J. Biomed. Inform., 54 (2015), 220–229.
  • 24. S. Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems, Neural Comput. Appl., 27 (2016), 1053–1073.
  • 25. G. Huang, G. B. Huang, S. Song, et al., Trends in extreme learning machines: a review, Neural Networks, 61 (2015), 32–48.
  • 26. P. L. Bartlett, The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network, IEEE T. Inform. Theory, 44 (2002), 525–536.
  • 27. Q. Y. Zhu, A. K. Qin, P. N. Suganthan, et al., Evolutionary extreme learning machine, Pattern Recogn., 38 (2005), 1759–1763.
  • 28. D. Dua, and E. K. Taniskidou, UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science, 2017. Available from: http://archive.ics.uci.edu/ml.
  • 29. Y. Wang, A. Wang, Q. Ai, et al., A novel artificial bee colony optimization strategy-based extreme learning machine algorithm, Prog. Artif. Intell., 6 (2016), 1–12.

 

Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved