Adaptive Identification and State Estimation of unknown nonlinear Stochastic Systems by Recurrent Neuralfuzzy Networks
Zhemin Wu, Chris J. Harris · ePrints Soton (University of Southampton) · 1997
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.