A new algorithm for online structure and parameter adaptation of RBF networks.

This paper deals with the problem of online adaptation of radial basis function (RBF) neural networks. A new adaptive training method is presented, which is able to modify both the structure of the network (the number of nodes in the hidden layer) and the output weights, as the algorithm proceeds. These adaptation capabilities make the algorithm suitable for modeling dynamical time varying systems, where not only the dynamics but also the operating region changes with time. Therefore, the important issue of extrapolation is faced successfully, but at the same time the algorithm takes care of the size of the network, by deleting the hidden node centers that remain inactive for a long time. The selection of the network centers is based on a fuzzy partition of the input space, which defines a number of fuzzy subspaces. The algorithm considers the centers of the fuzzy subspaces as candidates for becoming hidden node centers and makes the selections, so that at least one center is close enough to each input example. The proposed technique is illustrated through the application to time varying dynamical systems and is compared to other adaptive training methods.