Training of Time Series Patterns Using Recurrent Neural Networks Based on the Extended Kalman Filter

Abstract

Recent study, the extended Kalman filter (EKF) is applied to the learning algorithm for a feedforward neural network and it is shown that the EKF based learning algorithm has good convergence performances. However, the feedforward neural network could not perform a dynamical signal processing such as time series pattern recognition. On the other hand, the recurrent neural network (RNN) could have dynamical characteristics because the RNN has feedback connections with time delay in the network. The EKF based learning algorithms for the RNN are also reported and some training properties became clear. In this paper, RNN training based on the EKF are applied to the time series signal processing. The connection weights of the RNN can be modified by using the filtering algorithm based on the EKF. Simulation results show that the RNN based on the EKF has good performances of the training for time series patterns generated from sine functions and for some pulse patterns.