MURAL - Maynooth University Research Archive Library



    Experiment design considerations for non-linear system identification using neural networks


    Doherty, Sean and Gomm, J.B. and Williams, D. (1997) Experiment design considerations for non-linear system identification using neural networks. Computers & Chemical Engineering, 21 (3). pp. 327-346. ISSN 0098-1354

    [img]
    Preview
    Download (1MB) | Preview


    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...



    Add this article to your Mendeley library


    Abstract

    Although the non-linear modelling capability of neural networks is widely accepted there remain many issues to be addressed relating to the design of a successful identification experiment. In particular, the choices of process excitation signal, data sample time and neural network model structure all contribute to the success, or failure, of a neural network's ability to reliably approximate the dynamic behaviour of a process. This paper examines the effects of these design considerations in an application of a multi-layered perceptron neural network to identifying the non-linear dynamics of a simulated pH process. The importance of identification experiment design for obtaining a network capable of both accurate single step and long range predictions is illustrated. The use of model parsimony indices, model validation tests and histogram analysis of training data for design of a neural network identification experiment are investigated.

    Item Type: Article
    Keywords: Experiment design considerations; non-linear system identification; neural networks;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 8712
    Identification Number: https://doi.org/10.1016/S0098-1354(96)00003-8
    Depositing User: Seán Doherty
    Date Deposited: 17 Jul 2018 14:59
    Journal or Publication Title: Computers & Chemical Engineering
    Publisher: Elsevier
    Refereed: Yes
    URI:

    Repository Staff Only(login required)

    View Item Item control page

    Downloads

    Downloads per month over past year