Neurofuzzy Algorithms for Model Identification: Structure and Parameter Determination
Martin Brown, K.M. Bossley, Chris J. Harris · ePrints Soton (University of Southampton) · 1996
This paper describes some of the issues associated with developing a class of neurofuzzy construction algorithms based on B-spline fuzzy membership functions. These techniques have many desirable properties and links can be made with more conventional statistical model building approaches. The neurofuzzy model is decomposed into its linear and nonlinear components and a search technique is used to identify the structural nonlinearities whereas standard linear optimisation algorithms are used to identify the linear parameters. This paper discusses the performance of these two elements and contrasts their roles in the context of neurofuzzy systems.