The radial basis function network (RBFN) is a multi-layer feed-forward neural network consisting of simple processing elements (neurons) with weighted interconnections between the elements. The weighted interconnections between the neurons can be calculated by any one of a wide range of optimisation algorithms. We will demonstrate that although the gradient descent method produces a "better" solution to the sample problem, limiting the number of allowable interconnection weights enhances the tolerance of the network to errors in the DOE reconstruction. In addition, the genetic algorithm optimisation method is significantly faster than the gradient descent method for the same number of allowable weight values.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Optoelectronic radial basis function network training techniques


    Beteiligte:
    Waddie, A.J. (Autor:in) / Taghizadeh, M.R. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    652401 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Topology Optimization Using Hyper Radial Basis Function Network

    Apte, Aditya P. / Wang, Bo Ping | AIAA | 2008



    Radial Basis Function Artificial Neural-Network-Inspired Numerical Solver

    Wilkinson, Matthew C. / Meade, Andrew J. | AIAA | 2016



    3D Topology Optimization Using Hyper Radial Basis Function Network

    Apte, A. / Wang, B. | British Library Conference Proceedings | 2009