Skip to main content
SpringerLink
Log in

Fast-forward solver for inhomogeneous media using machine learning methods: artificial neural network, support vector machine and fuzzy logic

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Encountering with a nonlinear second-order differential equation including ϵ r and μ r spatial distributions, while computing the fields inside inhomogeneous media, persuaded us to find their known distributions that give exact solutions. Similarities between random distributions of electric properties and known functions lead us to estimate them using three mathematical tools of artificial neural networks (ANNs), support vector machines (SVMs) and Fuzzy Logic (FL). Assigning known functions after fitting with minimum error to arbitrary inputs using results of machine learning networks leads to achieve an approximate solution for the field inside materials considering boundary conditions. A comparative study between the methods according to the complexity of the structures as well as the accuracy and the calculation time for testing of unforeseen inputs, including classification, prediction and regression is presented. We examined the extracted pairs of ϵ r and μ r with ANN, SVM networks and FL and got satisfactory outputs with detailed results. The application of the presented method in zero reflection subjects is exemplified.

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

Similar content being viewed by others

References

  1. Didenkulova I, Pelinovsky E, Soomere T (2008) Exact travelling wave solutions in strongly inhomogeneous media. Estonian J Eng 14(3):220–231

    Article  Google Scholar 

  2. Sobhy MI, Bedair SS, Keriakos MH (1982) State-space approach for the analysis of networks containing lossy coupled transmission lines in inhomogeneous media. In: IEE Proceedings G-Electronic Circuits and Systems, vol 129, no 3, pp 89–95, IET

  3. Chen A-S, Leung MT, Daouk H (2003) Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Comput Oper Res 30(6):901–923

    Article  MATH  Google Scholar 

  4. Bongini P, Laeven L, Majnoni G (2002) How good is the market at assessing bank fragility? A horse race between different indicators. J Bank Finance 26(5):1011–1028

    Article  Google Scholar 

  5. Lee M-C, To C (2010) Comparison of support vector machine and back propagation neural network in evaluating the enterprise financial distress. arXiv Prepr. arXiv:1007.5133

  6. Chen A-S, Leung MT (2004) Regression neural network for error correction in foreign exchange forecasting and trading. Comput Oper Res 31(7):1049–1068

    Article  MATH  Google Scholar 

  7. Frank RJ, Davey N, Hunt SP (2001) Time series prediction and neural networks. J Intell Robot Syst 31(1–3):91–103

    Article  MATH  Google Scholar 

  8. Kim T-H (2010) Pattern recognition using artificial neural network: a review. In: International conference on information security and assurance, Springer, Berlin, Heidelberg, pp 138–148

  9. Özgür L, Güngör T, Gürgen F (2004) Spam mail detection using artificial neural network and Bayesian filter. In: International Conference Intelligent data engineering and automated learning, Springer, Berlin, Heidelberg, pp 505–510

  10. Narendra VG, Abdorrazzaghi M (2013) An intelligent system for identification of Indian Lentil types using Artificial Neural Network (BPNN). IOSR J Comput Eng (IOSRJCE) 15(5):54–60

    Article  Google Scholar 

  11. Mattioli FER, Lamounier EA Jr, Cardoso A, Soares AB, Andrade AO (2011) Classification of EMG signals using artificial neural networks for virtual hand prosthesis control. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pp 7254–7257

  12. Hoskins JC, Himmelblau DM (1992) Process control via artificial neural networks and reinforcement learning. Comput Chem Eng 16(4):241–251

    Article  Google Scholar 

  13. Yuan S-F, Chu F-L (2006) Support vector machines-based fault diagnosis for turbo-pump rotor. Mech Syst Signal Process 20(4):939–952

    Article  Google Scholar 

  14. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, pp 144–152

  15. Jonsson K, Kittler J, Li YP, Matas J (2002) Support vector machines for face authentication. Image Vis Comput 20(5):369–375

    Article  Google Scholar 

  16. Lu J, Plataniotis KN, Venetsanopoulos AN (2001) Face recognition using feature optimization and ν-support vector learning. In: Neural networks for signal processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop, pp 373–382

  17. Gorgevik D, Cakmakov D, Radevski V (2001) Handwritten digit recognition by combining support vector machines using rule-based reasoning. In: Proceedings of the 23rd International Conference on Information Technology Interfaces, 2001. ITI 2001, pp 139–144

  18. Shin CS, Kim KI, Park MH, Kim HJ (2000) Support vector machine-based text detection in digital video. In: Neural networks for signal processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, vol 2, pp 634–641

  19. Dong X, Zhaohui W (2001) Speaker recognition using continuous density support vector machines. Electron Lett 37(17):1

    Article  Google Scholar 

  20. Hong P, Tian Q, Huang TS (2000) Incorporate support vector machines to content-based image retrieval with relevance feedback. In: 2000 International conference on image processing, 2000. Proceedings, vol 3, pp 750–753

  21. Guo G, Zhang H-J, Li SZ (2001) Distance-from-boundary as a metric for texture image retrieval. In: 2001 IEEE international conference on acoustics, speech, and signal processing, 2001. Proceedings. (ICASSP’01), 2001, vol 3, pp 1629–1632

  22. Tian Q, Hong P, Huang TS (2000) Update relevant image weights for content-based image retrieval using support vector machines. In: 2000 IEEE international conference on multimedia and expo, 2000. ICME 2000, 2000, vol 2, pp 1199–1202

  23. Van Gestel T, Suykens JAK, Baestaens D-E, Lambrechts A, Lanckriet G, Vandaele B, De Moor B, Vandewalle J (2001) Financial time series prediction using least squares support vector machines within the evidence framework. Neural Networks, IEEE Trans. 12(4):809–821

    Article  Google Scholar 

  24. Frontzek T, Lal TN, Eckmiller R (2001) Predicting the nonlinear dynamics of biological neurons using support vector machines with different kernels. In: Proceedings. IJCNN’01. International joint conference on neural networks, 2001, vol 2, pp 1492–1497

  25. Mlynek DJ, Patyra MJ (2012) Fuzzy logic: implementation and applications. Springer, New York

  26. Terano T, Asai K, Sugeno M (1992) Fuzzy systems theory and its applications. Academic Press Professional, Inc., San Diego

    MATH  Google Scholar 

  27. Zadeh LA (1965) Information and control. Fuzzy sets 8(3):338–353

    Google Scholar 

  28. Chew WC (1995) Waves and fields in inhomogeneous media, vol 522. IEEE press, New York

    Google Scholar 

  29. Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12(6):783–789

    Article  Google Scholar 

  30. Tanyer SG, Karaman M, Ozturk I (1998) Analysis of wave propagation in inhomogeneous media using FDTD method and its applications. In: 1998 International Conference on mathematical methods in electromagnetic theory, 1998. MMET 98, vol 2. IEEE, pp 629–631

  31. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2):121–167

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECE Department, University of Alberta, #206, 9935, 83 Ave, Edmonton, AB, Canada

    Mohammad Abdolrazzaghi

  2. ECE Department, Iran University of Science and Technology, Tehran, Iran

    Soheil Hashemy & Ali Abdolali

Authors
  1. Mohammad Abdolrazzaghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soheil Hashemy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Abdolali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Abdolrazzaghi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdolrazzaghi, M., Hashemy, S. & Abdolali, A. Fast-forward solver for inhomogeneous media using machine learning methods: artificial neural network, support vector machine and fuzzy logic. Neural Comput & Applic 29, 1583–1591 (2018). https://doi.org/10.1007/s00521-016-2694-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2694-9

Keywords

Search

Navigation