Improved training of cellular SRN using Unscented Kalman Filtering for ADP

Cellular Simultaneous Recurrent Network (CSRN) is a unique type of recurrent networks that is designed to solve complex optimization problems. This network has already shown to successfully solve many challenging problems such as 2D maze navigation, image registration and affine transformation, game of go, and power system voltage profile prediction. One of the main challenges of using a complex network structure as CSRN is to efficiently train the network. Many representative training algorithms such as Back-propagation Through Time (BPTT), Extended Kalman Filtering (EKF) and Particle Swarm Optimization (PSO) have been used to train CSRN. Our prior works with CSRN suggest that for large number of network inputs, which is very common for large scale maze and image data, computational complexity of computing Jacobian in EKF training becomes prohibitive. In this paper, we propose Unscented Kalman Filter (UKF) for the training of CSRN to avoid computing Jacobian. We show that CSRN trained with UKF can solve the 2D maze traversal problem with better convergence rate than that of EKF. We also report preliminary results on binary image affine transformation wherein CSRN trained with UKF offers comparable performance to that of EKF. A comparison has been obtained between CSRN with GMLP core versus an Elman core trained with UKF for Affine transform results. Finally, we show that for more complex applications such as large scale image processing, UKF is much faster than EKF in training CSRN.