Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)
Sherif Botros, Christopher Atkeson
We examine the ability of radial basis functions (RBFs) to generalize. We compare the performance of several types of RBFs. We use the inverse dy(cid:173) namics of an idealized two-joint arm as a test case. We find that without a proper choice of a norm for the inputs, RBFs have poor generalization properties. A simple global scaling of the input variables greatly improves performance. We suggest some efficient methods to approximate this dis(cid:173) tance metric.