Abstract: A fuzzy counter-propagation (FCP) neural network, which is a generalized model of the counter-propagaton (CP) network, is proposed in this paper by defining output of the competitive unit of CP network as a fuzzy membership function. FCP not only is able to overcome the shortcomings of CP, but has the ability of universal function approximation as well. In view of network structure, FCP is equivalent to the radial basis function(RBF) network. In fact, FCP is an RBF network, also a fuzzy basis function network. In the end of this paper, the experiment to apply FCP to time series prediction shows that FCP outperforms CP and RBF in learning precision and generalizaton ability.