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人工智能是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学,是对人的意识、思维过程的模拟。粒计算是当前智能信息处理领域中一种新的概念和计算范式,是研究基于多层次粒结构的思维方式、复杂问题求解、信息处理模式及其相关理论、技术和工具的方法论。本文首先分析了人工智能模拟人脑智能的粒计算模式与方法,其次总结了粗糙集、商空间、模糊集、云模型、三支决策等几种典型的粒计算基本构架与数学模型,然后分析知识的多粒度解析表示与不确定性度量的研究现状,最后展望了粒计算求解模式在大数据时代所面临的机遇与挑战。
Artificial intelligence is a new technological science that studies and develops theories, methods, techniques and application systems that are used to simulate, extend and extend people’s intelligence and is a simulation of people’s consciousness and thinking process. Particle counting is a new concept and computing paradigm in the field of intelligent information processing. It is a methodology to study the thinking mode, complex problem solving, information processing mode and related theories, techniques and tools based on multi-level granular structure. In this paper, we firstly analyze the grain computing model and method of artificial intelligence to simulate human brain intelligence. Then we summarize some typical granular computing frameworks and mathematical models of rough set, quotient space, fuzzy set, cloud model and three decision-making methods, and then analyze The multi-granularity analysis of knowledge and the status quo of the measurement of uncertainty are discussed. Finally, the opportunities and challenges in the era of big data for grain computing are forecasted.