Adaptive kernels for data recovery in tele-haptic and tele-operation environments

The development of non-linear filtering through the use of kernel machines has gained much popularity in recent years. Both the kernel least-mean-squared (KLMS) [1], and kernel recursive least-mean-squared (KRLS) [2] have been used to provide superior regression performance to traditional linear methods. As well, there has been developments in on-line support vector machine techniques that allow non-linear regression to previously off-line batch methods [3] [4]. In this paper we present a novel adaptive method for tuning the kernel parameter of a Gaussian kernel when using the KLMS algorithm. We test our algorithm on both simulated, and real data captured from a haptic device.