Probabilistic Universal Learning Network

Universal Learning Network (ULN) has been reported, which is a framework for the modelling and control of the nonlinear large-scale complexed systems such as physical, social and economical phenomena. And a generalized learning algorithm has been proposed for ULN, which can be used in a unified manner for almost all kinds of networks such as static/dynamic networks, layered/recurrent type networks, time delay neural networks and the networks with multi-branches. But, as the signals transmitted through the ULN should be deterministic, the stochastic signals which are contaminated with noise can not be propagated through the ULN. In this paper, Probabilistic Universal Learning Network (PrULN) is presented, where a new learning algorithm to optimize the criterion function is defined on the stochastic dynamic systems. By using PrULN, the following are expected; (1) the generalization capability of the learning networks will be improved, (2) more sophisticated stochastic control will be obtained than the conventional stochastic control, (3) designing problems for the complex systems such as chaotic systems are expected to develop, whereas now the main research topics for the chaotic systems are only the analysis of the systems.