A Divide-and-Conquer Based Radial Basis Function Network with Application to Recurrent Function 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 as its input. The output of this new architecture is a linear combination of the sub-networks' 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.