Bossley, K.M., Brown, M. and Harris, C.J.
Bayesian Regularisation Applied to Neurofuzzy models
At EUFIT '96.
Full text not available from this repository.
Due to the inherent structure of neurofuzzy systems they are prone to poor generalisation. This paper discusses two complementary methods which should be employed to maximally exploit the available linguistic and numerical data, to overcome this problem. The main emphasis is the application of Bayesian regularisation to additive B-spline neurofuzzy models. This produces models which generalise well with poor quality data and hence identify more reliable rule bases. Error bars are then used to identify possible weakness in the resulting rule base, which require further validation.
Conference or Workshop Item
|Venue - Dates:
||EUFIT '96, 1996-01-01
||Southampton Wireless Group
||04 May 1999
||18 Apr 2017 00:24
|Further Information:||Google Scholar|
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