Comparing the performance of three neural classifiers for use in embedded applications

Yuhua Li, MJ Pont, CR Parikh, NB Jones

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

    Abstract

    In this paper, we provide a detailed empirical comparison of three neural-based classifiers used in embedded applications. The three techniques (multi-layer Perceptrons, radial basis function networks and adaptive fuzzy systems) are compared with one another and with a classical kNN classifier. In this study, we observe that the MLP provides similar levels of performance to the RBFN, AFS land kNN) classifiers while exerting a lower computational load on the processor.
    LanguageEnglish
    Title of host publicationUnknown Host Publication
    Pages34-39
    Number of pages6
    Publication statusPublished - 2000
    EventSOFT COMPUTING TECHNIQUES AND APPLICATIONS -
    Duration: 1 Jan 2000 → …

    Publication series

    NameADVANCES IN SOFT COMPUTING

    Conference

    ConferenceSOFT COMPUTING TECHNIQUES AND APPLICATIONS
    Period1/01/00 → …

    Fingerprint

    Classifiers
    Radial basis function networks
    Multilayer neural networks
    Fuzzy systems

    Keywords

    • multi-layer perceptron network
    • radial basis function network
    • adaptive fuzzy system
    • k-nearest neighbour
    • embedded system

    Cite this

    Li, Y., Pont, MJ., Parikh, CR., & Jones, NB. (2000). Comparing the performance of three neural classifiers for use in embedded applications. In Unknown Host Publication (pp. 34-39). (ADVANCES IN SOFT COMPUTING).
    Li, Yuhua ; Pont, MJ ; Parikh, CR ; Jones, NB. / Comparing the performance of three neural classifiers for use in embedded applications. Unknown Host Publication. 2000. pp. 34-39 (ADVANCES IN SOFT COMPUTING).
    @inproceedings{a757d27333aa4cf1bd9befeac9ddf116,
    title = "Comparing the performance of three neural classifiers for use in embedded applications",
    abstract = "In this paper, we provide a detailed empirical comparison of three neural-based classifiers used in embedded applications. The three techniques (multi-layer Perceptrons, radial basis function networks and adaptive fuzzy systems) are compared with one another and with a classical kNN classifier. In this study, we observe that the MLP provides similar levels of performance to the RBFN, AFS land kNN) classifiers while exerting a lower computational load on the processor.",
    keywords = "multi-layer perceptron network, radial basis function network, adaptive fuzzy system, k-nearest neighbour, embedded system",
    author = "Yuhua Li and MJ Pont and CR Parikh and NB Jones",
    note = "Workshop 99 on Recent Advances in Soft Computing, LEICESTER, ENGLAND, JUL 01-02, 1999",
    year = "2000",
    language = "English",
    series = "ADVANCES IN SOFT COMPUTING",
    pages = "34--39",
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    Li, Y, Pont, MJ, Parikh, CR & Jones, NB 2000, Comparing the performance of three neural classifiers for use in embedded applications. in Unknown Host Publication. ADVANCES IN SOFT COMPUTING, pp. 34-39, SOFT COMPUTING TECHNIQUES AND APPLICATIONS, 1/01/00.

    Comparing the performance of three neural classifiers for use in embedded applications. / Li, Yuhua; Pont, MJ; Parikh, CR; Jones, NB.

    Unknown Host Publication. 2000. p. 34-39 (ADVANCES IN SOFT COMPUTING).

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

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    AU - Parikh, CR

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    N1 - Workshop 99 on Recent Advances in Soft Computing, LEICESTER, ENGLAND, JUL 01-02, 1999

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    KW - radial basis function network

    KW - adaptive fuzzy system

    KW - k-nearest neighbour

    KW - embedded system

    M3 - Conference contribution

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    Li Y, Pont MJ, Parikh CR, Jones NB. Comparing the performance of three neural classifiers for use in embedded applications. In Unknown Host Publication. 2000. p. 34-39. (ADVANCES IN SOFT COMPUTING).