Fuzzy neural Petri-net method for reliability estimation of complex systems
复杂系统可靠性估计的模糊神经Petri网方法

A Fuzzy neural Petri-net method (FNPN) for estimating the reliability of complex systems is proposed. The definition and fire-rules of this FNPN are presented. A learning algorithm is put forward. This FNPN combines the advantages of fuzzy Petri-net and neural network. It can express and process the knowledge-based system of fuzzy productive rules, and posseses the capability of learning. The structure and parameters of the system can be determined by learning the sample data. An example of non-directional network is used to demonstrate the feasibility of this method.