Skip to main content
SpringerLink
Log in

Constrained PSO Based Center Selection for RBF Networks Under Concurrent Fault Situation

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In the training of radial basis function (RBF) networks, one important issue is to select RBF centers before constructing the networks. Most existing center selection methods are designed for the fault-free situation only. However, as the implementation of the networks may be perturbed by faults, these algorithms may lead to networks with degraded performance. This paper considers the center selection problem for RBF networks under the concurrent fault situation where multiplicative weight noise and open weight fault exist simultaneously. In particular, we introduce a binary label vector indicating the centers selected from training samples. Using the label vector, the fault-tolerant RBF model under the concurrent fault situation is reformulated as a constrained optimization problem, so that fault-tolerance can be considered in the procedure of center selection. To solve this constrained optimization problem, a constrained particle swarm optimization based algorithm is developed to select centers and train the network simultaneously. Simulation results show that the proposed algorithm is superior than state-of-the-art center selection algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Burr JB (1991) Digital neural network implementations. In: Neural networks, concepts, applications, and implementations, vol 3, pp 237–285

  2. Chen S (1995) Nonlinear time series modelling and prediction using gaussian RBF networks with enhanced clustering and RLS learning. Electron Lett 31(2):117–118

    Article  MathSciNet  Google Scholar 

  3. Chen S (2006) Local regularization assisted orthogonal least squares regression. Neurocomputing 69(4–6):559–585

    Article  Google Scholar 

  4. Chen S, Cowan CFN, Grant P (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2(2):302–309

    Article  Google Scholar 

  5. Ekambaram R, Fefilatyev S, Shreve M, Kramer K, Hall LO, Goldgof DB, Kasturi R (2016) Active cleaning of label noise. Pattern Recognit 51:463–480

    Article  Google Scholar 

  6. Feng RB, Leung CS, Constantinides AG (2016) LCA based RBF training algorithm for the concurrent fault situation. Neurocomputing 191:341–351

    Article  Google Scholar 

  7. Feng X, Chen GL, Guo WZ (2006) Settings and experimental analysis of acceleration coefficients in particle swarm optimization algorithm. J Jimei Univ 2:146–151

    Google Scholar 

  8. Han Z, Feng RB, Wan WY, Leung CS (2015) Online training and its convergence for faulty networks with multiplicative weight noise. Neurocomputing 155:53–61

    Article  Google Scholar 

  9. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, vol. 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  10. Kennedy J, Eberhart R (2002) Particle swarm optimization. In: ICNN’95-international conference on neural networks

  11. Leung CS, Sum JF (2012) RBF networks under the concurrent fault situation. IEEE Trans Neural Netw Learn Syst 23(7):1148–1155

    Article  Google Scholar 

  12. Leung CS, Wan WY, Feng R (2016) A regularizer approach for RBF networks under the concurrent weight failure situation. IEEE Trans Neural Netw Learn Syst 28(6):1360–1372

    Article  Google Scholar 

  13. Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml

  14. Nawrocki RA, Voyles RM (2011) Artificial neural network performance degradation under network damage: stuck-at faults. In: Proceedings of the international joint conference on neural networks (IJCNN), pp 442–449

  15. Orr MJ, et al (1996) Introduction to radial basis function networks. Technical Report, Center for Cognitive Science, University of Edinburgh

  16. Phatak D, Koren I (1995) Complete and partial fault tolerance of feedforward neural nets. IEEE Trans Neural Netw 6(2):446–456

    Article  Google Scholar 

  17. Poggio T, Girosi F (1990) Networks for approximation and learning. Proc IEEE 78(9):1481–1497

    Article  Google Scholar 

  18. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Article  Google Scholar 

  19. Shi Y, Eberhart RC (2002) Empirical study of particle swarm optimization. In: Congress on evolutionary computation

  20. Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst 9:155–161

    Google Scholar 

  21. Stevenson M, Winter R, Widrow B (1990) Sensitivity of feedforward neural networks to weight errors. IEEE Trans Neural Netw 1(1):71–80

    Article  Google Scholar 

  22. Sugiyama M, Ogawa H (2002) Optimal design of regularization term and regularization parameter by subspace information criterion. Neural Netw 15(3):349–361

    Article  Google Scholar 

  23. Tufekci P (2014) Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods. Int J Electr Power Energy Syst 60:126–140

    Article  Google Scholar 

  24. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  Google Scholar 

  25. Wang H, Feng R, Han Z, Leung C (2018) ADMM-based algorithm for training fault tolerant RBF networks and selecting centers. IEEE Trans Neural Netw Learn Syst 29(8):3870–3878

    Article  MathSciNet  Google Scholar 

  26. Yeh IC (2006) Analysis of strength of concrete using design of experiments and neural networks. J Mater Civil Eng 18(4):597–604

    Article  Google Scholar 

  27. Yu H, Reiner P, Xie T, Bartczak T, Wilamowski B (2014) An incremental design of radial basis function networks. IEEE Trans Neural Netw 25(10):1793–1803

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, Jiangsu, China

    Jing Dong

  2. College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China

    Yuxin Zhao & Chang Liu

Authors
  1. Jing Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuxin Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jing Dong.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by the National Natural Science Foundation of China (61906087), the Natural Science Foundation of Jiangsu Province of China (BK20180692), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China (17KJB510025), the Natural Science Foundation of China (41676088), the Natural Science Foundation of Heilongjiang Province of China (QC2017067), and the Major Basic Research Program for National Security of China (973 Program for National Defence, No. 613317).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, J., Zhao, Y. & Liu, C. Constrained PSO Based Center Selection for RBF Networks Under Concurrent Fault Situation. Neural Process Lett 51, 2437–2451 (2020). https://doi.org/10.1007/s11063-020-10202-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-020-10202-1

Keywords

Search

Navigation