Neural modelling of dynamic systems with non-measurable state variables

Abstract

The paper deals with neural modelling of dynamic processes. Attention is focused on processes characterised by non-measurable states and their modelling with nonlinear recurrent neural networks. A relationship is developed which, for such models, correlates the actual prediction error with the past ones.