Using self-organizing incremental neural network (SOINN) For radial basis function networks

This paper presents a batch learning algorithm and an online learning algorithm for radial basis function networks based on the self-organizing incremental neural network (SOINN), together referred to as SOINN-RBF. The batch SOINN-RBF is a combination of SOINN and least square algorithm. It achieves a comparable performance with SVM for regression. The online SOINN-RBF is based on the self-adaption procedure of SOINN and adopts the growing and pruning strategy of the minimal resource allocation network (MRAN). The growing and pruning criteria use the redefined significance, which is originally introduced by the growing and pruning algorithm for RBF (GGAP-RBF). Simulation results for both artificial and real-world data sets show that, comparing with other online algorithms, the online SOINN-RBF has comparable approximation accuracy, network compactness and better learning efficiency.