rough numbers
英 [rʌf ˈnʌmbəz]
美 [rʌf ˈnʌmbərz]
网络 大概数字; 粗糙数
双语例句
- A rough neural network method is proposed to solve the problems in an information system with interval numbers.
针对具有区间数的信息系统，提出用粗糙神经网络求解其问题的方法。 - Three decomposition theorems and a representation theorem of rough fuzzy numbers are given.
讨论了粗糙模糊数（RFN）的构造性质，给出了粗糙模糊数的分解定理及表现定理。 - On the base of fuzzy number and rough sets, this paper defines the rough fuzzy number ( RF number). Then it researchs the nature of RF number and discusses the distance between two RF numbers.
定义了粗糙模糊数（RFN），进而研究了其性质及二RF数间的距离，可用于在实际问题中对模糊信息的表述及信息加工。 - Decomposition Theorem and Representation Theorem on Rough Fuzzy Numbers
粗糙模糊数的分解定理和表现定理 - Using concepts of the upper ( lower) approximation in the rough set theory and the definition of confidence degree, random parameters in an engineering structure could be transferred into the interval numbers.
根据粗糙集理论中的上、下近似集概念和随机参数的定义，可以把结构分析中的随机参数转化为一定置信度意义下的区间数。 - The Closeness of Rough Fuzzy Numbers
粗糙模糊数的贴近度 - Based on these, the closeness of distance, the least and greatest closeness, the least average closeness and the closeness in the lattice for rough fuzzy numbers are given.
在此基础上，分别给出了粗糙模糊数的距离贴近度，最大最小贴近度，最小平均贴近度以及格贴近度。 - A novel rough set-based approach to ordering rule acquisition in fuzzy information systems with triangular fuzzy numbers is presented.
针对一类带有三角模型的模糊信息系统，提出一种基于粗糙集的有序规则获取方法。 - Traditional rough set model is based on completion of available information, ignoring incompletion and statistical information of available information, so we can not discover uncertain decision-preferential information hiding in large numbers of decision-making instances by traditional rough set model.
传统的粗糙集（RoughSet）模型基于可利用信息的完全性，忽视了可利用信息中的不完全性和可能存在的统计信息，因而不能发现隐藏在大量决策实例中的不确定性决策偏好信息。 - A Fault Verification Method of the Rough Neural Network with Interval Numbers
具有区间数的粗糙神经网络故障认定方法 - In section 3.4 the relationship of rough inclusion and rough equality of rough intervals are defined by two kinds of tools which are respectively the lower ( upper) approximation operator in real numbers domain and rough membership functions.
利用实数域上的上下近似算子及粗糙隶属函数两种工具，第四节分别定义粗糙区间的粗包含与粗相等关系，并分析其诸多性质。 - A rough neural network method is proposed to the decision system with interval numbers. The topologic structure and learning algorithm of the rough neural network are given. The approximation theorem of the rough neural network is proved.
对于决策信息系统中属性值为区间数的情况，文中提出了一种粗糙神经网络的方法，给出了这种粗糙神经网络的网络结构及学习算法，证明了此网络的逼近定理。