Local bayesian regularisation of parsimonious neurofuzzy models for real world dynamic processes

摘要

By combining properties of fuzzy systems and neural networks, neurofuzzy modelling is ideally suited to many system identification and data modelling applications. Recently, data-driven model construction algorithms have been developed to identify these models. These algorithms have proved essential for producing accurate parsimonious models. However, due to problems with sparse data and restricted model structures, models with high model variance are often produced. Thus resulating in models which generalise poorly.