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    A SUPERIOR TRAINING STRATEGY FOR THREE-LAYER FEEDFORWARD ARTIFICIAL NEURAL NETWORKS

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    Author
    Hsu, Kuo-Lin
    Gupta, Hoshin Vijai
    Sorooshian, Soroosh
    Affiliation
    Department of Hydrology & Water Resources, The University of Arizona
    Issue Date
    1996-03
    
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    Publisher
    Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ)
    Rights
    Copyright © Arizona Board of Regents
    Collection Information
    This title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.
    Abstract
    A new algorithm is proposed for the identification of three-layer feedforward artificial neural networks. The algorithm, entitled LLSSIM, partitions the weight space into two major groups: the input- hidden and hidden -output weights. The input- hidden weights are trained using a multi -start SIMPLEX algorithm and the hidden -output weights are identified using a conditional linear- least- square estimation approach. Architectural design is accomplished by progressive addition of nodes to the hidden layer. The LLSSIM approach provides globally superior weight estimates with fewer function evaluations than the conventional back propagation (BPA) and adaptive back propagation (ABPA) strategies. Monte -carlo testing on the XOR problem, two function approximation problems, and a rainfall- runoff modeling problem show LLSSIM to be more effective, efficient and stable than BPA and ABPA.
    Series/Report no.
    Technical Reports on Hydrology and Water Resources, No. 96-030
    Sponsors
    This research was partially supported by grants from the Hydrologic Research Laboratory of the U.S. National Weather Service (Grant no. NA37WH0385), the NASA -EOS Interdisciplinary Research Program (IDP -88 -086), and the NOAA Research Program (NA16RC0119 -0). The first author greatly appreciates the fellowship support provided by the NASA Global Change Program (Grant No. NGT- 30045).
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    Hydrology & Water Resources Technical Reports

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