Adaptive Identification and State Estimation of unknown nonlinear Stochastic Systems by Recurrent Neuralfuzzy Networks

Wu, Z.Q. and Harris, C.J. (1997) Adaptive Identification and State Estimation of unknown nonlinear Stochastic Systems by Recurrent Neuralfuzzy Networks. 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics. 455--460 .

Record type: Conference or Workshop Item (Other)

Abstract

In this paper, the authors utilise the neural network technique and the Kalman filter algorithm to achieve adaptive and optimal modelling and filtering for unknown observable nonlinear stochastic processes. A special class of state space model is imposed on the input-output observable nonlinear stochastic system, which can be identified by a recurrent neurofuzzy network. This model enables the conventional Kalman filter to estimate the system state, avoiding the convergent problem associated with the extended Kalman filter. The training algorithm and the Kalman filter are executed interactively, thus adaptive modelling and filtering is achieved. A simulated example is given to illustrate the approach.

This record has no associated files available for download.