Adaptive state estimation using dynamic recurrent neural networks

The estimation of states from input and output measurements using linear state-space models has been widely studied. In particular, the Kalman filtering algorithm has found many applications, such as time-series forecasting, control, parameter estimation, and fault diagnosis. In this paper we propose a new method for adaptive state estimation using feedforward and recurrent neural networks and, in particular, for state filtering that is applicable to general nonlinear systems. The developed method has been applied to the problem of estimating the parameters of an electromechanical system consisting of a DC motor and a centrifugal pump with the associated pumping system. Finally, the proposed algorithm is used in estimating the states and a critical parameter of a complex process system, namely a heat exchanger