On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Sequential Monte Carlo Methods to Train Neural Network Models
Neural Computation
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
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Diagonal recurrent neural network (DRNN) is widely applied to nonlinear identification. In this paper, the extended Kalman filter and particle filter are firstly combined to train DRNN. Utilizing time windows, a method to evaluate the dynamical performance of DRNN is presented. Network weights of particles are optimized by the resampling algorithm. The high convergent speed and high training precision are obtained by the new algorithm. Simulation results of the nonlinear dynamical identification verify the validity of the new algorithm.