Programmable analogue VLSI for radial basis function networks

Radial basis function (RBF) networks are finding increasing use in applications involving multidimensional function interpolation and pattern classification. The major obstacle to the wider use of RBFs is the complex nature of their calculations, in particular the requirement to evaluate Euclidean distance repeatedly. Almost no hardware implementations have been reported. The authors present analogue VLSI circuits which can calculate the Euclidean norm, and compute programmable width basis functions of this norm. These novel circuits can be combined with existing ´conventional´ neural circuits, to implement complete RBF networks.