Learning Petri network with route control
Kotaro Hirasawa, Shozaburo Oka, Shintaro Sakai, M. Obayashi, Junichi Murata · 2002
Large-scale complicated systems are required to be controlled timely and appropriately. A human brain has similar functions to those of a controller of the large-scale complicated systems; it scans and recognizes sensory inputs and outputs responses to the environments. Why does a human brain work skillfully? The key is the capability of functions distribution and learning. Functions distribution means that a specific part exists in the brain, in order to realize a specific function. For example, a live neural network has different acting parts corresponding to different network inputs or stimuli. In this paper, we have proposed a new brain-like model that we call learning Petri network (LPN). The fundamental idea is to revise Petri net. Petri net is composed of state and transition and can control firing by tokens, so it is possible for this net to realize functions distribution. The revising point is to give Petri net the ability of learning as neural network (NN). And, it is the fundamental difference from NN, that learning of the proposed method is carried out on the only network pass of the token transfer.