This paper proposes a Recurrent Radial Basis Function network (RRBFN) that can be applied to temporal pattern classifications and predictions. Based on the architecture of the conventional Radial Basis Function networks, the RRBFNs use Gaussian nonlinearity and have feedback paths between every hidden node. These feedback paths enable the networks to learn temporal patterns without an input buffer to hold the recent elements of an input sequence. A gradient descent learning algorithm for the RRBFNs is derived. Two RRBFNs with different number of hidden nodes were tested using a temporal sequence generated by an infinite impulse response filter. The results show that the RRBFNs can approximate the filter more accurate than the Continually Running Fully Recurrent networks trained by the Real-Time Recurrent Learning algorithm.
ASJC Scopus subject areas
- Artificial Intelligence