A new neuro-based state estimator

This paper presents a new neuro-computing approach to the problem of state estimation. The neuro-estimator is established by a hybrid combination of a Hopfield network and a feedforward multilayer neural net. The former is very useful for optimization and the latter is well known for function approximation. This neuro-estimator is very appropriate for real-time implementation of nonlinear state estimators especially when modeling uncertainty is included.