抄録
Recently, Neural Network (N.N.) modeling neuro circuits in a brain is going to be studied actively and are applied to various fields. An idea on controlling large-scale complicated systems by N.N. can be used, but it is difficult for N.N. to control the route of the network in response to input data in order to obtain better performance.
On the other hand, Petri Net is composed of state, which can express the state of the systems, and transitions, which can express various processing, and it can also control firing of the transition by tokens. So Petri Net can realize functions distribution. Functions distribution means that a specific part exists in order to realize a specific function.
In this paper, a new network model called Learning Petri Network (L.P.N.) is presented. The fundamental idea is to give Petri Net the ability of learning as N.N.. Therefore, the new network is a learning network with functions distribution that can control the route of the network in response to input data.
It is shown that L.P.N. is superior in performance to N.N. in the point of forming nonlinear discontinuous functions and identifying nonlinear systems including relay elements.
