A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications

Yuhua Li, MJ Pont, NB Jones

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.
    LanguageEnglish
    Title of host publicationUnknown Host Publication
    Pages577-592
    Number of pages6
    Publication statusPublished - 1999
    EventCONDITION MONITORING `99, PROCEEDINGS - SWANSA, WALES
    Duration: 1 Jan 1999 → …

    Conference

    ConferenceCONDITION MONITORING `99, PROCEEDINGS
    Period1/01/99 → …

    Fingerprint

    Radial basis function networks
    Condition monitoring
    Multilayer neural networks
    Failure analysis
    Microcontrollers

    Keywords

    • engine misfire detection
    • neural networks
    • multi-layer perception
    • radial basis function
    • condition monitoring
    • fault classification

    Cite this

    @inproceedings{1189705bdbe948ab9de9ef0322c48b02,
    title = "A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications",
    abstract = "In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.",
    keywords = "engine misfire detection, neural networks, multi-layer perception, radial basis function, condition monitoring, fault classification",
    author = "Yuhua Li and MJ Pont and NB Jones",
    note = "International Conference on Condition Monitoring, SWANSA, WALES, APR 12-15, 1999",
    year = "1999",
    language = "English",
    pages = "577--592",
    booktitle = "Unknown Host Publication",

    }

    Li, Y, Pont, MJ & Jones, NB 1999, A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications. in Unknown Host Publication. pp. 577-592, CONDITION MONITORING `99, PROCEEDINGS, 1/01/99.

    A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications. / Li, Yuhua; Pont, MJ; Jones, NB.

    Unknown Host Publication. 1999. p. 577-592.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    TY - GEN

    T1 - A comparison of the performance of radial basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications

    AU - Li, Yuhua

    AU - Pont, MJ

    AU - Jones, NB

    N1 - International Conference on Condition Monitoring, SWANSA, WALES, APR 12-15, 1999

    PY - 1999

    Y1 - 1999

    N2 - In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.

    AB - In this paper, we provide a detailed comparison of multi-layer Perceptron (MLP) and radial basis function (RBF) networks in embedded, microcontroller-based condition monitoring and fault diagnosis applications. On the basis of the studies presented here, it is concluded that the MLP provides similar levels of performance to the RBF network while exerting a low computational load on the processor.

    KW - engine misfire detection

    KW - neural networks

    KW - multi-layer perception

    KW - radial basis function

    KW - condition monitoring

    KW - fault classification

    M3 - Conference contribution

    SP - 577

    EP - 592

    BT - Unknown Host Publication

    ER -