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Knowledge extraction from a mixed transfer function artificial neural network

Khan, Imad M., Frayman, Yakov and Nahavandi, Saeid 2006, Knowledge extraction from a mixed transfer function artificial neural network, Journal of advanced computational intelligence and intelligent informatics, vol. 10, no. 3, pp. 295-301.

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Title Knowledge extraction from a mixed transfer function artificial neural network
Author(s) Khan, Imad M.
Frayman, Yakov
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Journal of advanced computational intelligence and intelligent informatics
Volume number 10
Issue number 3
Start page 295
End page 301
Publisher Fuji Technology Press Ltd
Place of publication Tokyo, Japan
Publication date 2006
ISSN 1343-0130
Keyword(s) neural networks
mixed transfer functions
knowledge extraction
Summary One of the main problems with Artificial Neural Networks (ANNs) is that their results are not intuitively clear. For example, commonly used hidden neurons with sigmoid activation function can approximate any continuous function, including linear functions, but the coefficients (weights) of this approximation are rather meaningless. To address this problem, current paper presents a novel kind of a neural network that uses transfer functions of various complexities in contrast to mono-transfer functions used in sigmoid and hyperbolic tangent networks. The presence of transfer functions of various complexities in a Mixed Transfer Functions Artificial Neural Network (MTFANN) allow easy conversion of the full model into user-friendly equation format (similar to that of linear regression) without any pruning or simplification of the model. At the same time, MTFANN maintains similar generalization ability to mono-transfer function networks in a global optimization context. The performance and knowledge extraction of MTFANN were evaluated on a realistic simulation of the Puma 560 robot arm and compared to sigmoid, hyperbolic tangent, linear and sinusoidal networks.
Language eng
Indigenous content off
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004037

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.