Dynamic subgrouping in RTRL provides a faster O(N/sup 2/) algorithm
Static grouping of processing elements (PEs) has been proposed to reduce the computational complexity of real time recurrent learning (RTRL) from O(n/sup 4/) to O(n/sup 2/), but performance suffers. This paper proposes a dynamic subgrouping of PEs estimated from a local approximation of the /spl pi/ matrix based on temporal Hebbian of sensitivities during training. The method is O(n/sup 2/) and leads to better performance.