Advertisement

Fuzzy and Neuro-fuzzy Techniques for Modelling and Control

  • S. H. Lee
  • R. J. Howlett
  • S. D. Walters
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)

Abstract

This paper presents comparative evaluation of two fuzzy-derived techniques for modelling fuel spray penetration in the cylinder of a diesel internal combustion engine and a fuzzy control system for a small internal combustion engine. The first technique used a pure fuzzy paradigm, the parameters of this technique are a collection of intuitively comprehensible rules and fuzzy-set membership functions. While the visual nature of this system facilitates the optimisation of the parameters, the need for this to be accomplished manually is a disadvantage. The second technique used an adaptive neuro-fuzzy inference system (ANFIS), where automatic adjustment of the system parameters was effected by a neural network based on prior knowledge. The ANFIS exhibited improved accuracy compared to a pure fuzzy model. It also has the advantage over the pure fuzzy paradigm that the need for the human operator to tune the system by adjusting the bounds of the membership functions is removed. Future work is concentrating on the establishment of an improved neuro-fuzzy paradigm for adaptive, fast and accurate control of small internal combustion engines.

Keywords

Membership Function Fuzzy Logic Fuzzy System Fuzzy Inference System Fuzzy Logic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kalogirou, S.A.: Applications of artificial neural-networks for energy systems. Appl. Energy 67, 17–35 (2000)CrossRefGoogle Scholar
  2. 2.
    Xu, K., Luxmoore, A.R., Jones, L.M., Deravi, F.: Integration of neural networks and expert systems for microscopic wear particle analysis. Knowledge-Based Systems 11, 213–227 (1998)CrossRefGoogle Scholar
  3. 3.
    Zadeh, L.A.: Fuzzy sets. Information Control 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Wang, L.X.: Fuzzy systems are universal approximators. In: Proceedings of the IEEE International conference on Fuzzy Systems, pp. 1163–1170 (1992)Google Scholar
  5. 5.
    Jang, J.: ANFIS: Adaptive network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics 23, 665–685 (1993)CrossRefGoogle Scholar
  6. 6.
    Lee, S.H., Howlett, R.J., Walters, S.D.: An Adaptive Neuro-fuzzy Modelling of Diesel Spray Penetration, Paper No. SAE-NA 2005-24-64. In: Seventh International Conference on Engines for Automobile (ICE 2005), Capri, Napoli, Italy (September 2005) ISBN 88-900399-2-2 Google Scholar
  7. 7.
    Lee, S.H., Howlett, R.J., Walters, S.D.: Small Engine Control by Fuzzy Logic. International Journal of Intelligent & Fuzzy Systems, Volume 15, Numbers 3(4), 207–217 (2004)Google Scholar
  8. 8.
    Copp, D.G., Burnham, K.J., Locket, F.P.: Model Comparison for Feedforward Air/fuel Ratio Control. In: KACC International Conference on Control, vol. 9, pp. 670–675 (1998)Google Scholar
  9. 9.
    Powers, W.F., Nicastri, P.R.: Automotive vehicle control challenges in the 21st century. Control Engineering Practice 8, 605–618 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. H. Lee
    • 1
  • R. J. Howlett
    • 1
  • S. D. Walters
    • 1
  1. 1.Intelligent Systems & Signal Processing Laboratories, Engineering Research CentreUniversity of BrightonMoulsecoomb, BrightonUK

Personalised recommendations