Knowledge extraction from a mixed transfer function artificial neural network
Contribuinte(s) |
Alo, Richard |
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Data(s) |
01/01/2004
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Resumo |
One of the big problems with Artificial Neural Networks (ANN) is that their results are not intuitively clear. For example, if we use the traditional neurons, with a sigmoid activation function, we can approximate any function, including linear functions, but the coefficients (weights) in this approximation will be rather meaningless. To resolve this problem, this paper presents a novel kind of ANN with different transfer functions mixed together. The aim of such a network is to i) obtain a better generalization than current networks ii) to obtain knowledge from the networks without a sophisticated knowledge extraction algorithm iii) to increase the understanding and acceptance of ANNs. Transfer Complexity Ratio is defined to make a sense of the weights associated with the network. The paper begins with a review of the knowledge extraction from ANNs and then presents a Mixed Transfer Function Artificial Neural Network (MTFANN). A MTFANN contains different transfer functions mixed together rather than mono-transfer functions. This mixed presence has helped to obtain high level knowledge and similar generalization comparatively to monotransfer function nets in a global optimization context.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
University of Houston-Downtown |
Relação |
http://dro.deakin.edu.au/eserv/DU:30005552/frayman-knowledgeextraction-2004.pdf http://www.intech.scitech.au.edu/register/Intech09/main.asp |
Tipo |
Conference Paper |