Peak stick RBF network for online system identification

In many practical problems of online system identification, the distribution of observed samples is uneven. For instance, at points where system is idle or changes slowly, the sample density increases and where system moves quickly, it is reduced. This generally results in performance degradation of learning. We will propose a new algorithm for training RBF networks that is particularly developed for online learning with uneven sample distribution. The basic idea is to find peaks and stick to them. Experiments show a notable improvement in convergence rate, settling of weights and error minimization.