Estimation of unmeasured inputs using recurrent neural networks and the extended Kalman filter

A multiple-input-multiple-output dynamic system, whose inputs can not be measured online but data for the training of a neural network can be made available, is considered. A method of estimating those online immeasurable inputs is proposed based on a recurrent neural network model of the system and using the extended Kalman filter (EKF). An experimental stirred tank heater and a simulation model of a drying process are employed to demonstrate the effectiveness of the method. The two systems are modeled using recurrent neural networks and the estimation of an unmeasured valve position in the case of the stirred tank heater and an unmeasured moisture content for the drying process is carried out using the EKF algorithm. The experimental and the simulation results substantiate the practical effects of the proposed method for a class of dynamic systems.