A neuro-fuzzy-evolutionary classifier of low-risk investments

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1777

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DC Field	Value	Language
dc.contributor.author	Kalganova, T	-
dc.contributor.author	Serguieva, A	-
dc.date.accessioned	2008-03-03T14:02:07Z	-
dc.date.available	2008-03-03T14:02:07Z	-
dc.date.issued	2002	-
dc.identifier.citation	The Eleventh IEEE International Conference on Fuzzy Systems, pp 997-1002, IEEE Press, 2002	en
dc.identifier.isbn	0780372808	-
dc.identifier.issn	10987584	-
dc.identifier.uri	http://bura.brunel.ac.uk/handle/2438/1777	-
dc.description.abstract	This paper demonstrates that a hybrid fuzzy neural network can serve as a classifier of low risk investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is applied to empirical data on UK companies traded on the LSE	en
dc.format.extent	740262 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.publisher	IEEE	en
dc.subject	Evolutionary computation	en
dc.subject	Fuzzy neural nets	-
dc.title	A neuro-fuzzy-evolutionary classifier of low-risk investments	en
dc.type	Research Paper	en
dc.identifier.doi	http://dx.doi.org/10.1109/FUZZ.2002.1006640	-
Appears in Collections:	Business and Management Electronic and Computer Engineering Brunel Business School Research Papers

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