摘要
语言模型具有很好的可理解性特征,但在多数情况下,其精确性是难满足要求的.本文利用改进型微粒群算法(MPSO)优化输入变量的语言值及对应的正交模糊集参数,再应用Wang方法以形成语言模型,在保持可理解性情况下,获得较精确的语言模型.改进型微粒群算法采用惯性权重自适应动态调整策略,结果显示该改进算法在语言模型过程中更容易获得全局最优解,学习效率和优化性能明显提高.
Linguistic model behaves an Interpretable characteristic , but in many case,it is not accurate to a sufficient degree. The paper makes use of the modified particle swarm algorithm（MPSO） to be used to optimize each orthodoxy membership function of linguistic terms form each variable, form linguistic model by Wang way. The accuracy of the model obtained is improved while maintaining their descriptive power. MPSO adopts a strategy of dynamically self- adiusting inertia parameter, results show that the modified particle swarm algorithm has great efficiency and better performance than PSO in learning linguistie model.
出处
《小型微型计算机系统》
CSCD
北大核心
2006年第12期2306-2309,共4页
Journal of Chinese Computer Systems
基金
湖南省自然科学基金项目(04JJY6036)资助.
