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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/507

Title: Function learning using wavelet neural networks
Authors: SHASHIDHARA, HL
LOHANI, SUMIT
GADRE, VM
Keywords: function approximation
learning (artificial intelligence)
neural nets
signal processing
wavelet transforms
Issue Date: 2000
Publisher: IEEE
Citation: Proceedings of IEEE International Conference on Industrial Technology (V 1) Goa, India, 19-22 January 2000, 335-340
Abstract: A new architecture based on wavelets and neural networks is proposed and implemented for learning a class of functions. The performance of such networks is analyzed for function learning. These functions belong to a common class but possess minor variations. The scheme developed makes use of wavelet neural network. It is useful to have a small dimensional network that can approximate a wide class of functions. The network has two levels of freedom. By this the network not only selects the parameters of the basis wavelets but also provides a variation in the choice.
URI: http://hdl.handle.net/10054/507
http://dspace.library.iitb.ac.in/xmlui/handle/10054/507
ISBN: 0-7803-5812-0
Appears in Collections:Proceedings papers

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