Unscented grid filtering and elman recurrent networks

This paper develops an unscented grid-based filter for improved recurrent neural network modeling of time series. The filter approximates directly the weight posterior distribution as a linear mixture using deterministic unscented sampling. The weight posterior is obtained in one step, without linearisation through derivatives. An expectation maximisation algorithm is formulated for evaluation of the complete data likelihood and finding the state noise and observation noise hyperparemeters. Empirical investigations show that the proposed unscented grid filter compares favourably to other similar filters on recurrent network modeling of two real-world time series of environmental importance.