[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Fuzzy Logic and Neuro-fuzzy Modelling of Diesel Spray Penetration

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3682))

Abstract

This paper describes a comparative evaluation of two fuzzy-derived techniques for modelling fuel spray penetration in the cylinders of a diesel internal combustion engine. The first model is implemented using conventional fuzzy-based paradigm, where human expertise and operator knowledge were used to select the parameters for the system. The second model used an adaptive neuro-fuzzy inference system (ANFIS), where automatic adjustment of the system parameters is effected by a neural networks based on prior knowledge. Two engine operating parameters were used as inputs to the model, namely in-cylinder pressure and air density. Spray penetration length was modelled on the basis of these two inputs. The models derived using the two techniques were validated using test data that had not been used during training. The ANFIS model was shown to achieve an improved accuracy compared to a pure fuzzy model, based on conveniently selected parameters.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Kalogirou, S.A.: Applications of artificial neural-networks for energy systems. Appl. Energy 67, 17–35 (2000)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Zadeh, L.A.: Fuzzy sets. Information Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  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. Jang, J.: ANFIS: Adaptive network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics 23, 665–685 (1993)

    Article  Google Scholar 

  6. Howlett, R.J., de Zoysa, M.M., Walters, S.D., Howson, P.A.: Neural Network Techniques for Monitoring and Control of Internal Combustion Engines. In: Int. Symposium on Intelligent Industrial Automation (1999)

    Google Scholar 

  7. Baba, N., Sato, K.: A Consideration on the Learning Algorithm of Neural Network. In: Knowledge-Based Intelligent Electronic system (KES 1998), South Australia, pp. 7–12 (1998)

    Google Scholar 

  8. Harris, C.J., Brown, M., Bossley, K.M., Mills, D.J., Feng, M.: Advances in Neurofuzzy Algorithms for Real-time Modelling and Control. Engineering Applications of Artificial Intelligence 9(1), 1–16 (1996)

    Article  Google Scholar 

  9. Masters, T.: Practical Neural Network Recipes in C++. Academic Press, London (1993)

    Google Scholar 

  10. Heywood, J.B.: Internal Combustion Engine Fundamentals. McGraw-Hill, New York (1988)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems & Signal Processing Laboratories, Engineering Research Centre, University of Brighton, Moulsecoomb, Brighton, BN2 4GJ, UK

    Shaun H. Lee, Bob R. J. Howlett, Simon D. Walters & Cyril Crua

Authors
  1. Shaun H. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bob R. J. Howlett
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon D. Walters
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cyril Crua
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lee, S.H., Howlett, B.R.J., Walters, S.D., Crua, C. (2005). Fuzzy Logic and Neuro-fuzzy Modelling of Diesel Spray Penetration. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_88

Download citation

  • DOI: https://doi.org/10.1007/11552451_88

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28895-4

  • Online ISBN: 978-3-540-31986-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation