Exploring recurrent learning for neurofuzzy networks using regularization theory
Qiang Gan · 2003
This paper establishes a relation between recurrent neurofuzzy networks and regularized neurofuzzy networks, providing a natural and analytical way to explain why recurrent networks are better at multi-step prediction than feedforward networks. As a benefit from the established relation, a strategy for compromising the multi-step prediction ability and the divergence tendency in recurrent learning is developed.