In this paper different neural networks (NN) are compared for modelling a variable valve timing spark-ignition (VVT SI) engine. The overall system is divided for each output into five neural multi-input single output (MISO) subsystems. Three kinds of NN, multilayer Perceptron (MLP), pseudo-linear radial basis function (PLRBF), and local linear model tree (LOLIMOT) networks, are used to model each subsystem. Real data were collected when the engine was under different operating conditions and these data are used in training and validation of the developed neural models. The obtained models are finally tested in a real-time online model configuration on the test bench. The neural models run independently of the engine in parallel mode. The model outputs are compared with process output and compared among different models. These models performed well and can be used in the model-based engine control and optimization, and for hardware in the loop systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Modelling a variable valve timing spark ignition engine using different neural networks


    Contributors:
    Beham, M (author) / Yu, D L (author)


    Publication date :

    2004-10-01


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Modelling a variable valve timing spark ignition engine using different neural networks

    Beham,M. / Yu,D.L. / Liverpool John Moores Univ.,GB | Automotive engineering | 2004



    Electronic continuous variable valve timing for small spark ignition engine

    Menzel,C. / Torresan,C. / Knight,J. et al. | Automotive engineering | 2008


    Thermodynamic analysis of variable valve timing influence on spark ignition engine efficiency

    Moro,D. / Ponti,F. / Serra,G. et al. | Automotive engineering | 2001