摘要
语言动力系统以语言中的词作为运算对象,这为描述复杂大系统提供了一种有效手段。然而,用语言描述事物具有很强的不确定性,这使得语言动力系统在具体实现时面临严峻挑战。覆盖粗糙集在处理不确定问题中有着独特的优势,可用其来解决语言动力系统中的不确定问题。为此,将语言中的词用覆盖块的形式来表示,再利用覆盖粗糙集中的上下近似思想建立状态方程、输出方程和反馈控制的上、下近似映射,得到基于覆盖粗糙集的语言动力系统模型,进一步给出模型在分析与解决问题时的具体推理方法。实例分析证明了所建模型及推理方法的正确性和有效性。
Linguistic dynamic systems （LDS） make computing and reasoning by using words. In this way, LDS pro- vides an effective measure to describe large complex systems. However, words have major uncertainties with descri- bing things. This causes serious challenges for LDS. Covering-based rough sets have distinctly unique advantages in dealing with uncertain problems. This paper details how they can be effective with solving the problems in LDS. Firstly, the words in languages are represented by using the form of covering blocks; secondly, mappings of the state equation, output equation and feedback control are established by using the thought of the lower and upper ap- proximations, then a LDS model based on the covering-based rough sets is obtained; thirdly, an inference method of the model is proposed to analyze and solve real problems; finally, the validity and the efficiency of the model and the inference method have been proved by some instance analysis.
出处
《智能系统学报》
CSCD
北大核心
2014年第2期229-234,共6页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金资助项目(61170128)
福建省自然科学基金资助项目(2011J01374
2012J01294)
新疆财经大学博士启动基金资助项目
关键词
粗糙集理论
人工智能
数据挖掘
语言学
控制理论
粒计算
近似理论
知识获取
rough set theory
artificial intelligence
data mining
linguistics
control theory
granular computing
approximation theory
knowledge acquisition
