  Cellular Simultaneous Recurrent Neural Network (SRN) has been shown to be a
function approximator more powerful than the MLP. This means that the
complexity of MLP would be prohibitively large for some problems while SRN
could realize the desired mapping with acceptable computational constraints.
The speed of training of complex recurrent networks is crucial to their
successful application. Present work improves the previous results by training
the network with extended Kalman filter (EKF). We implemented a generic
Cellular SRN and applied it for solving two challenging problems: 2D maze
navigation and a subset of the connectedness problem. The speed of convergence
has been improved by several orders of magnitude in comparison with the earlier
results in the case of maze navigation, and superior generalization has been
demonstrated in the case of connectedness. The implications of this
improvements are discussed.
