TY - GEN ID - cogprints496 UR - http://cogprints.org/496/ A1 - Smagt, P. van der A1 - Groen, F. A1 - Groenewoud, F. van het Y1 - 1994/// N2 - We present a method for accurate representation of high-dimensional unknown functions from random samples drawn from its input space. The method builds representations of the function by recursively splitting the input space in smaller subspaces, while in each of these subspaces a linear approximation is computed. The representations of the function at all levels (i.e., depths in the tree) are retained during the learning process, such that a good generalisation is available as well as more accurate representations in some subareas. Therefore, fast and accurate learning are combined in this method. The method, which is applied to hand-eye coordination of a robot arm, is shown to be superior to other neural networks. PB - IEEE KW - neural networks KW - high-dimensional function approximation KW - learning KW - robot arm control TI - The locally linear nested network for robot manipulation SP - 2787 AV - public EP - 2792 ER -