Knowledge extraction from a mixed transfer function artificial neural network Khan, I., Frayman, Yakov and Nahavandi, Saeid 2004, Knowledge extraction from a mixed transfer function artificial neural network, in InTech'04 : Proceedings of the 5th International Conference on Intelligent Technologies, University of Houston-Downtown, Houston, Tx, pp. 1-6. Attached Files Name Description MIMEType Size Downloads Title Knowledge extraction from a mixed transfer function artificial neural network Author(s) Khan, I. Frayman, Yakov Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name International Conference on Intelligent Technologies (5th : 2004 : Houston, Texas) Conference location Houston, Texas Conference dates 2-4 December 2004 Title of proceedings InTech'04 : Proceedings of the 5th International Conference on Intelligent Technologies Editor(s) Alo, Richard Publication date 2004 Start page 1 End page 6 Publisher University of Houston-Downtown Place of publication Houston, Tx Summary 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. Language eng Field of Research 090699 Electrical and Electronic Engineering not elsewhere classified HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30005552 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Engineering and Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 03 Dec 2008, 13:29:36 EST Wed, 01 Apr 2009, 14:01:41 EST Wed, 06 May 2009, 12:03:40 EST Tue, 08 Jun 2010, 11:10:10 EST Tue, 08 Jun 2010, 11:12:03 EST Fri, 17 Oct 2014, 08:42:12 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 953 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 07 Jul 2008, 09:51:08 EST

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