A divide-and-conquer based radial basis function network with application to recurrent modelling

In this paper, a new architecture of divide-and-conquer based radial basis function network (DCRBF) and its learning algorithm are presented. The DCRBF network is a hybrid system consisting of several sub-RBF networks, each of which individually takes a sub-input space at its input. The output of this new architecture is linear combination of the sub-network´s outputs with the coefficients tuned together with each sub-network system parameters. Since this system divides a high-dimensional modelling problem into several low-dimensional ones, it can considerably reduce the structural complexity of a RBF network, whereby the net´s learning speed as a whole is significantly improved with the comparable generalization ability. We apply DCRBF to model a recurrent version of RBF networks. The experimental results have shown its outstanding performance.