抄録
Universal Learning Network(ULN) which is a generalized Neural Network, can be used to model and control large scale complicated systems. But ULN can not be applied to discrete event systems. Therefore a discrete event oriented learning network which is called Automaton Learning Network(ALN) has been already proposed. ALN has the same structure as ULN has. In this paper. a generalized type of ALN named Probability Automaton Learning Network(PrALN) and Possibility Automaton Learning Network(PoALN) are presented in order to realize an ALN with non-deterministic nature. In the simulations with a relatively simple model, we studied the fundamental characteristics of PrALN and PoALN.
