Some practical aspects on incremental training of RBF network for robot behavior learning

The radial basis function (RBF) neural network with Gaussian activation function and least-mean squares (LMS) learning algorithm is a popular function approximator widely used in many applications due to its simplicity, robustness, optimal approximation, etc.. In practice, however, making the RBF network (and other neural networks) work well can sometimes be more of an art than a science, especially concerning parameter selection and adjustment. In this paper, we address three issues, namely the normalization of raw sensory-motor data, the choice of receptive fields for the RBFs, and the adjustment of the learning rate when training the RBF network in incremental learning fashion for robot behavior learning, where the RBF network is used to map sensory inputs to motor outputs. Though these issues are less theoretical and scientific, they are more practical, and sometimes more crucial for the application of the RBF network to the problems at hand. We believe that being aware of these practical issues can enable a better use of the RBF network in the real-world application.