天津科技大学学报Issue(3)：68-72,5.DOI:10.13364/j.issn.1672-6510.2014.03.014
模糊神经Petri网算法优化及其收敛性分析
Optimization of Fuzzy Neural Petri Net Algorithm and Convergence Analysis
摘要
Abstract
Aimed at the poor computational accuracy and convergence as well as too violent network concussion during the training of fuzzy neural Petri net(FNPN)learning algorithm,an optimized algorithm was proposed. Two S-type continuous functions were used to express transition enablement and the new tag values after transition firing.In addition,the value before correction was considered,and then the new momentum was added based on the traditional parameter correction for-mula,which can ensure the convergence of the proposed optimization algorithm. It was proved that the optimized parameter correction algorithm can ensure the convergence of the FNPN network.关键词
Petri网/模糊Petri网/模糊神经Petri网/BP算法/收敛性Key words
Petri net/fuzzy Petri net/fuzzy neural Petri net/BP algorithm/convergence分类
信息技术与安全科学引用本文复制引用
李孝忠,周艳军..模糊神经Petri网算法优化及其收敛性分析[J].天津科技大学学报,2014,(3):68-72,5.基金项目
国家自然科学基金资助项目(61070021，11301382) （61070021，11301382）
