A novel method for online training of dynamic neural networks

Abstract

A fast, efficient, and novel way of online training of dynamic neural networks is presented in this paper. The method is based on a combination of recursive least-squares and backpropagation; in a large number of cases, backpropagation can be avoided altogether. The proposed method would be suitable for real-time identification, fault detection, and control of uncertain dynamic systems.