Neural simulation of Petri nets

✍ Scribed by S.W. Chen; C.Y. Fang; K.E. Chang

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 311 KB
• Volume: 25
• Category: Article
• ISSN: 0167-8191
• DOI: 10.1016/s0167-8191(98)00107-0

✦ Synopsis

Petri nets and neural networks share a number of analogies. Investigations of their relationships can be sorted into two categories: (a) the modeling of neural activities with Petri nets, and (b) the neural simulation of Petri nets. The work presented in this paper belongs to the second category. Unlike divide-and-conquer approaches, the proposed method settles the extraneous skeleton of simulators. Inherent distinctions of Petri nets are characterized by the individual constituents of simulators. The constructed simulators thus reveal a consistently uniform structure on a macroscopic level. Compared with those generated by the divide-andconquer approaches, ours look much portable and are empirically economic. Furthermore, in a fully parallel machine with enough nodes the overall time complexity of the neural simulator will be constant.