Skip to main content
SpringerLink
Log in

Vehicle inductive signatures recognition using a Madaline neural network

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

Abstract

In this paper, we report results obtained with a Madaline neural network trained to classify inductive signatures of two vehicles classes: trucks with one rear axle and trucks with double rear axle. In order to train the Madaline, the inductive signatures were pre-processed and both classes, named C2 and C3, were subdivided into four subclasses. Thus, the initial classification task was split into four smaller tasks (theoretically) easier to be performed. The heuristic adopted in the training attempts to minimize the effects of the input space non-linearity on the classifier performance by uncoupling the learning of the classes and, for this, we induce output Adalines to specialize in learning one of the classes. The percentages of correct classifications presented concern patterns which were not submitted to the neural network in the training process, and, therefore, they indicate the neural network generalization ability. The results are good and stimulate the maintenance of this research on the use of Madaline networks in vehicle classification tasks using not linearly separable inductive signatures.

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
Fig. 10

Similar content being viewed by others

References

  1. Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley, New York

    Google Scholar 

  2. Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, Oxford

    Google Scholar 

  3. US Department of Transportation/Federal Highway Administration (2006) Traffic detector handbook, 3rd edn, vol 1, chap 2, pp 1–56

  4. Mimbela LEY, Klein LA (2003) A summary of vehicle detection and surveillance technologies used in intelligent transportation systems. Vehicle Detector Clearinghouse Project—Federal Highway Administration’s (FHWA) Intelligent Transportation Systems Program Office

  5. Sun C, Ritchie SG (1999) Individual vehicle speed estimation using single loop inductive waveforms. J Transp Eng 125(6):531–538

    Article  Google Scholar 

  6. Ki Y-K, Baik D-K (2006) Model for accurate speed measurement using double-loop detectors. IEEE Trans Veh Technol 55(4):1094–1101

    Article  Google Scholar 

  7. Gajda J, Sroka R, Stencel M, Wajda A, Zeglen T (2001) A vehicle classification based on inductive loop detectors. In: Proceedings of the 18th IEEE instrumentation and measurement technology conference, Budapest, pp 460–464

  8. Oh C, Tok A, Ritchie SG (2005) Real-time freeway level of service using inductive-signature-based vehicle reidentification system. IEEE Trans Intell Transp Syst 6(2):138–146

    Article  Google Scholar 

  9. Sun CC, Arr GS, Ramachandran RP, Ritchie SG (2004) Vehicle reidentification using multidetector fusion. IEEE Trans Intell Transp Syst 5(3):155–164

    Article  Google Scholar 

  10. Oh S, Ritchie SG, Oh C (2002) Real time traffic measurement from single loop inductive signatures. In: 81st annual meeting of the Transportation Research Board, Washington, DC

  11. Sun C, Ritchie SG, Oh S (2003) Inductive classifying artificial network for vehicle type categorization. Comput-Aided Civil Infrastruct Eng 18:161–172

    Article  Google Scholar 

  12. Sun C (2000) An investigation in the use of inductive loop signatures for vehicle classification. California PATH Research Report-UCB-ITS-PRR-2000-4

  13. Ki Y-K, Baik D-K (2006) Vehicle-classification algorithm for single-loop detectors using neural networks. IEEE Trans Veh Technol 55(6):1704–1711

    Article  Google Scholar 

  14. Oh C, Ritchie SG (2007) Recognizing vehicle classification information from blade sensor signature. Pattern Recognit Lett 29(9):1041–1049

    Article  Google Scholar 

  15. Widrow B, Lehr MA (1990) 30 years of adaptive neural networks: perceptron, Madaline and backpropagation. Proc IEEE 78(9):1415–1442

    Article  Google Scholar 

  16. Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639

    Article  Google Scholar 

  17. Lima GRT, Silva JDS, Saotome O (2007) Morphological identification of inductive signatures using an Adaptive Resonance Theory based learning scheme. Learn Nonlinear Model-SBRN 5(1):20–35

    Google Scholar 

  18. Widrow B, Hoff ME (1960) Adaptive switching circuits, 1960 IRE Western electric show and convention record, Part 4, pp 96–104

  19. Winter R, Widrow B (1988) Madaline Rule II: a training algorithm for neural networks. In: IEEE International conference on neural networks, pp 401–408

  20. Ridgway WC III (1962) 30 An adaptive logic system with generalizing properties. PhD thesis, Stanford Electronics Labs Rep 1556-1, Stanford University, Stanford

  21. Linden A, Kindermann J (1989) Inversion of multilayer nets. In: Proceedings of international joint conference on neural network, Washington DC, vol II, pp 425–430

  22. Shepanski JF (1988) Fast learning in artificial neural systems: multilayer perceptron training using optimal estimation. In: Proceedings of IEEE international conference on neural network, San Diego, vol I, pp 465–472

  23. Song J, Hassoun MH (1990) Learning with hidden targets. In: Proceedings of international joint conference on neural network, San Diego, vol 3, pp 93–98

  24. Nilsson NJ (1990) The mathematical foundations of learning machines, Sect. 6.3. Morgan Kauffmann, San Mateo

    Google Scholar 

  25. Auer P, Burgsteiner H, Maass W (2008) A learning rule for very simple universal approximators consisting of a single layer of Perceptrons. Neural Netw 21:786–795

    Article  Google Scholar 

  26. Fausett L (1994) Fundamentals of neural networks: architectures, algorithms and applications. Prentice-Hall, Upper Saddle River

  27. Rumelhart DE, McClelland JL (1986) Parallel distributed processing: explorations in the microstructure of cognition, vol 1: foundations. MIT Press, Cambridge

    Google Scholar 

  28. http://www.isis.ecs.soton.ac.uk/resources/svminfo/

  29. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River

  30. Gori M, Tesi A (1992) On the problem of local minima in backpropagation. IEEE Trans Pattern Anal Mach Intell 14(1):76–86

    Article  Google Scholar 

  31. Bi W, Wang X, Zeng T, Tamura H (2005) Avoiding the local minima problem in backpropagation algorithm with modified error function. IEICE Trans Fundam E88-A(12):3645–3653

    Article  Google Scholar 

  32. Xiong J-J, Zhang H (2003) Research on the problem of neural network convergence. In: Proceedings of the IEEE second international conference on machine learning and cybernetics, Xi-an-China, vol 2, pp 1132–1134

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

    MATH  Google Scholar 

  34. Vapnik VN (1991) Principles of risk minimization for learning theory. Neural Inf Process Syst 4:831–838

  35. Mitra P, Murthy CA, Pal SK (2005) Active support vector learning with statistical queries. Support vector machines: theory and applications. Springer, Berlin, pp 99–111

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Programa de Pós-Graduação em Engenharia Eletrônica e Computação, Instituto Tecnológico de Aeronáutica-ITA, Praça Marechal Eduardo Gomes, 50, Vila das Acácias, São José dos Campos, SP, 12228-900, Brazil

    Glauston R. Teixeira de Lima

  2. Laboratório Associado de Computação e Matemática Aplicada-LAC, Instituto Nacional de Pesquisas Espacias-INPE, Av. dos Astronautas, 1758, Jardim da Granja, São José dos Campos, SP, 12227-010, Brazil

    José Demísio S. Silva

  3. Divisão de Engenharia Eletrônica e Computação, Instituto Tecnológico de Aeronáutica-ITA, Praça Marechal Eduardo Gomes, 50, Vila das Acácias, São José dos Campos, SP, 12228-900, Brazil

    Osamu Saotome

Authors
  1. Glauston R. Teixeira de Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Demísio S. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Osamu Saotome
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Glauston R. Teixeira de Lima.

Rights and permissions

Reprints and permissions

About this article

Cite this article

de Lima, G.R.T., Silva, J.D.S. & Saotome, O. Vehicle inductive signatures recognition using a Madaline neural network. Neural Comput & Applic 19, 421–436 (2010). https://doi.org/10.1007/s00521-009-0298-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-009-0298-3

Keywords

Search

Navigation