A Learning Method for Recurrent Networks Based on Minimization of Finite Automata

From the viewpoint of applying recurrent neural networks to AI, it is not suitable to use learning algorithms based on optimum control theory. One reason of this problem is lack of correspondence between these algorithms and traditional symbol processing. In this report, we propose a new algorithm for modi ed Elman networks (PEX model). This algorithm is derived from the minimization procedure of nite automata under the correspondence between Elman networks and nite automata. In this algorithm, a network predicts next network state by itself, and this prediction plays an important role in re ning the network. In addition, we examine this self-prediction from various viewpoints, and discuss about possibilities of this algorithm.