Analysis of neural networks in terms of domain functions

Berend Jan van der Zwaag, Cees Slump, Lambert Spaanenburg

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    Despite their success-story, artificial neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more as a mysterious "black box". Although much research has already been done to "open the box," there is a notable hiatus in known publications on analysis of neural networks. So far, mainly sensitivity analysis and rule extraction methods have been used to analyze neural networks. However, these can only be applied in a limited subset of the problem domains where neural network solutions are encountered. In this paper we propose a wider applicable method which, for a given problem domain, involves identifying basic functions with which users in that domain are already familiar, and describing trained neural networks, or parts thereof, in terms of those basic functions. This will provide a comprehensible description of the neural network's function and, depending on the chosen base functions, it may also provide an insight into the neural network' s inner "reasoning." It could further be used to optimize neural network systems. An analysis in terms of base functions may even make clear how to (re)construct a superior system using those base functions, thus using the neural network as a construction advisor.
    Original languageEnglish
    Title of host publication3rd IEEE Benelux Signal Processing Symposium (SPS-2002)
    Place of PublicationLeuven, Belgium
    PublisherKatholieke Universiteit
    Number of pages4
    Publication statusPublished - Mar 2002
    Event3rd IEEE Benelux Signal Processing Symposium, SPS-2002 - Leuven, Belgium
    Duration: 21 Mar 200222 Mar 2002
    Conference number: 3


    Conference3rd IEEE Benelux Signal Processing Symposium, SPS-2002
    Abbreviated titleSPS


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