Universal learning network and computation of its higher order derivatives

In this paper, the universal learning network (ULN) is presented, which models and controls large scale complicated systems such as industrial plants, economics, social and life phenomena. The computing method of higher order derivatives of ULN is derived in order to obtain the learning ability. The basic idea of ULN is that large scale complicated systems can be modeled by the network which consists of nonlinearly operated nodes and branches which may have arbitrary time delays including zero or minus ones. It is shown that the first order derivatives of ULN with sigmoid functions and one sampling time delays correspond to the backpropagation learning algorithm of recurrent neural networks