State estimation of nonlinear system through Particle Filter based Recurrent Neural Networks
N. Yadaiah, Raju Surampudi Bapi, Abhishek Vijaya Kumar, M. Roopchandan · 2011
This paper presents a Hybrid Particle Filter based RNN method for state estimation of non-linear dynamical system with knowledge of its input and output measurements. Particle filters are sequential Monte Carlo methods based on point mass (or particle) representations of probability densities, which is used to train Recurrent Neural Networks for estimation problems. The performance this method is compared with EKF based estimation and RNN based estimation. An Induction motor is considered as typical non-linear system and is implemented in MATLAB environment.