Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26391
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Swingler, Kevin
Contact Email: kevin.swingler@stir.ac.uk
Title: A Comparison of Learning Rules for Mixed Order Hyper Networks
Citation: Swingler K (2015) A Comparison of Learning Rules for Mixed Order Hyper Networks In: Proceedings of the 7th International Joint Conference on Computational Intelligence, Setubal, Portugal: Science and Technology Publications. NCTA (IJCCI), pp. 17-27.
Issue Date: 12-Nov-2015
Conference Name: NCTA (IJCCI)
Abstract: A mixed order hyper network (MOHN) is a neural network in which weights can connect any number of neurons, rather than the usual two. MOHNs can be used as content addressable memories with higher capacity than standard Hopfield networks. They can also be used for regression, clustering, classification, and as fitness models for use in heuristic optimisation. This paper presents a set of methods for estimating the values of the weights in a MOHN from training data. The different methods are compared to each other and to a standard MLP trained by back propagation and found to be faster to train than the MLP and more reliable as the error function does not contain local minima.
Status: Publisher version
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
URL: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220%2f0005588000170027

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