蚁群算法在模糊Petri网参数优化中的应用  

Application of ant colony algorithm in parameters optimization of fuzzy Petri nets

作　　者：李洋[1] 乐晓波[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,湖南长沙410076

出　　处：《计算机应用》2007年第3期638-641,共4页journal of Computer Applications

基　　金：湖南省教育厅自然科学基金资助项目(01JJY2061);湖南省教育厅科研基金资助项目(01C306)

摘　　要：如何确定模糊产生式规则的各项参数对模糊Petri网的建立意义重大。把蚁群算法中的最大-最小系统引入到模糊Petri网的参数寻优过程,提出一种基于线程实现技术的参数优化算法。该算法实现不依赖于经验数据,对初始输入无严格要求。仿真实例表明,经蚁群线程优化算法训练出的参数正确率较高,且所得的模糊Petri网具有较强的泛化能力和自适应功能。How to determine the various parameters of fuzzy production rules is significant to the built up of fuzzy Petri net （FPN）, which has not been solved yet. Maximum-minimum ant system （MMAS） of ant colony algorithm （ACA） was originally introduced into the procedure of exploring parameters of FPN. An optimization algorithm based on the techniques of multithreadlng was proposed. Realization of the algorithm did not depend on experiential data and no strict requirements for primary input were needed. Simulation shows that the trained parameters gained from above MMAS are highly accurate and the resultant FPN model possesses strong generalizing and self-adjusting capabilities.

关 键 词：模糊PETRI网 模糊推理 线程技术 蚁群算法 最大-最小蚁群系统 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TP302.7[自动化与计算机技术—计算机科学与技术]