EFI controllers approximate complex, non-linear relationships between fuelling parameters, engine states and ambient conditions using linked tables or 'maps'. Determination of a single fuelling value generally requires interrogation of a number of maps and linear interpolation to determine intermediate values. Some types of Artificial Neural Network (NN) can model multi-dimensional arbitrary functions to any desired degree of accuracy, and interpolate smoothly. This paper demonstrates how Multi-Layer Perceptrons can approximate fuelling maps and trim tables, to produce smooth surfaces that model the data with good interpolation performance.


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    Title :

    Neural networks - potential for enhanced control of automotive electronic fuel injection systems


    Additional title:

    Neuronale Netze bieten Potential für eine verbesserte Regelung elektronischer Kraftstoffeinspritzsysteme


    Contributors:
    Osman, K.A. (author) / Cole, A.C. (author) / Higginson, A.M. (author)


    Publication date :

    1999


    Size :

    14 Seiten, 16 Bilder, 16 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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