Bayesian Regularisation Applied to Neurofuzzy models
K.M. Bossley, Matthew Brown, Chris J. Harris · ePrints Soton (University of Southampton) · 1996
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.