The generic particle filter has been applied with success to neural network training

but the proposal distribution chosen by the generic particle filter doesnt incorporate the latest observations which can deteriorate the performance of the algorithm. In this paper

we propose to use the iterated extended Kalman filter to generate proposal distribution in particle filtering framework. The iterated extended Kalman filter can make efficient use of the latest observation

and the generated proposal distribution can approximate the posterior distribution of neural network weights much better and improve the performance of particle filter. The experimental results show that the proposed particle filter outperforms the generic particle filter and the EKPF.