A General Regression Neural Network

Abstract

This paper describes a memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface. This general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure. Even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from one observed value to another. The algorithmic form can be used for any regression problem in which an assumption of linearity is not justified. The parallel network form should find use in applications such as learning the dynamics of a plant model for prediction or control. © 1991 IEEE