Particle filter based neural network modeling of nonlinear systems for state space estimation

The system identification/modeling problem looks for a suitably parameterized model, representing a given process. The parameters of the model are adjusted to optimize a performance function based on error between the given process output and identified process output. The linear system identification field is well established with many classical approaches whereas most of those methods cannot be applied for nonlinear systems. The problem becomes tougher if the system is completely unknown with only the output time series is available. It has been reported that the capability of artificial neural network to approximate all linear and nonlinear input-output maps makes it predominantly suitable for the identification of nonlinear systems, where only the output time series is available. [1][2][4][5]. The work reported here is an attempt of modeling certain nonlinear systems using recurrent neural networks with extended Kalman filtering (EKF) and particle filtering (PF) approaches [19]. An assessment on the model performances in the mean square error (MSE) sense has also been done for both.