The Fault Diagnosis Model of FMS Workflow based on Adaptive Weighted Fuzzy Petri net

被引：1
|
作者：
Pan, HaiLan [1 ]
Jiang, WenRong [1 ]
He, HaiHui [1 ]
机构：
[1] Shanghai Second Polytech Univ, Sch Comp & Informat, Shanghai 201209, Peoples R China
来源：
ADVANCED DESIGNS AND RESEARCHES FOR MANUFACTURING, PTS 1-3 | 2013年 / 605-607卷
关键词：
FSM; Neural Network; Petri net; default diagnoses; Weight;
D O I：
10.4028/www.scientific.net/AMR.605-607.837
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper proposes a method for using neural network and weighted fuzzy Petri net to diagnose fault. Aiming at the traditional Petri net can not precisely predict the complex relation of the default phenomenon and the cause, neural network, fuzzy logic and the traditional Petri net are combined, and a constructing method for adaptive weighted fuzzy Petri net model is proposed. Based on this, an improved BP algorism is introduced to train the weight of the model, and the specific process for using the model to diagnose the fault is given. Finally, the model was applied to the instance of FMS, and the model was proved to have the advantages of Petri net and neural network and have reasoning and adaptive ability.
引用
收藏
页码：837 / 843
页数：7
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