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    Variable weight neural networks and their applications on material surface and epilepsy seizure phase classifications

    226508_146009_Variable_weight_neural_networks_and_their_applications_on_.pdf (1.480Mb)
    226507_135845_final_draft_Variable_weight_NNs_and_their_applications_on_material_surface.pdf (1013.Kb)
    Access Status
    Open access
    Authors
    Lam, H.K.
    Ekong, U.
    Xiao, B.
    Ouyang, G.
    Liu, H.
    Chan, Kit Yan
    Ling, S.
    Date
    2015
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Lam, H.K. and Ekong, U. and Xiao, B. and Ouyang, G. and Liu, H. and Chan, K.Y. and Ling, S. 2015. Variable weight neural networks and their applications on material surface and epilepsy seizure phase classifications. Neurocomputing. 149 (Part C): pp. 1177-1187.
    Source Title
    Neurocomputing
    DOI
    10.1016/j.neucom.2014.09.011
    ISSN
    0925-2312
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/38754
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a novel neural network having variable weights, which is able to improve its learning and generalization capabilities, to deal with classification problems. The variable weight neural network (VWNN) allows its weights to be changed in operation according to the characteristic of the network inputs so that it demonstrates the ability to adapt to different characteristics of input data resulting in better performance compared with ordinary neural networks with fixed weights. The effectiveness of the VWNN is tested with the consideration of two real-life applications. The first application is on the classification of materials using the data collected by a robot finger with tactile sensors sliding along the surface of a given material. The second application considers the classification of seizure phases of epilepsy (seizure-free, pre-seizure and seizure phases) using real clinical data. Comparisons are performed with some traditional classification methods including neural network, k-nearest neighbors and naive Bayes classification techniques. It is shown that the VWNN classifier outperforms the traditional methods in terms of classification accuracy and robustness property when input datais contaminated by noise.

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