Universal learning network and computation of its higher order derivatives

In this paper, Universal Learning Network (U.L.N.) is presented, which models and controls large scale complicated systems such as industrial plants, economic, social and life phenomena, and also a computing method of higher order derivatives of U.L.N. is derived in order to obtain learning ability. The basic idea of U.L.N. 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 has not been presented that the network such as U.L.N. is able to model and control naturally the large scale complicated systems which can be seen commonly in the social and physical worlds. It is shown that first order derivatives of U.L.N. with sigmoid functions and one sampling time delays correspond to the back propagation learning algorithm of the recurrent neural network.