Forward propagation universal learning network

Abstract

In this paper, a computing method of higher order derivatives of universal learning network (ULN) is derived by forward propagation, which models and controls large scale complicated systems such as industrial plants, economic, social and life phenomena. It is shown by comparison that forward propagation is preferable to backward propagation in computation time when higher order derivatives with respect to time invariant parameters should be calculated. It is also shown that first order derivatives of ULN with sigmoid functions and one sampling time delays correspond to the forward propagation learning algorithm of the recurrent neural networks. Furthermore, it is suggested that robust control and chaotic control can be realized if higher order derivatives are available.