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dynamic fuzzy machine learning system 动态模糊机器学习系统
Dynamic fuzzy machine learning model and its validation 动态模糊机器学习模型及验证
dynamic fuzzy machine learning 动态模糊机器学习
Adeli, Hojjat, Hung,Shih-Lin, “Machine Learning Neural Networks, Genetic Algorithm, and Fuzzy systems,” John Wiley &Sons, Ins., New York, 1995. 连俊杰，“使用遗传基因法则决定避震器最佳位置和参数的方法”，国立中兴大学机研所硕士论文，1994。
Adeli, H. and Hung ,S.L., 1995, “Machine Learning: Neural Networks, Genetic Algorithm, and Fuzzy systems,” John Wiley & Sons, Ins., New York. 连俊杰，1994，使用遗传基因法则决定避震器最佳位置和参数的方法，国立中兴大学机研所硕士论文。
Adeli, Hojjat, Hung, Shih-Lin, Machine learning：neural networks, genetic algorithms, and fuzzy systems, John Wiley & Sons, Ins., New York, 1995. 廖伟成，“应用基因演算法于直升机旋翼叶片之最佳化设计”，私立淡江大学航空太空工程研究所硕士论文，2001。
The introduce of CDF brings fuzzy property to the course of machine learning and recognition, which exactly reflects the fuzzy property of the thinking course of human brain. 转化程度函数的引入使得生成和识别过程带有了模糊性，这恰好反映了人脑思维过程本身带有的模糊性。
By taking advantages of machine learning,the ambig... 数值实验验证了本文方法的有效性和优越性。
Based on Dynamic Fuzzy Set, the Dynmic Fuzzy Measure theorem is proposed in this paper. 基于动态模糊集;提出动态模糊测度理论.
On the basis of a evolving clustering method (ECM), a new modeling approach of T-S type dynamic fuzzy inference model is proposed. 摘要以一种进化聚类算法(ECM)为基础，提出了一种新的T-S型动态模糊推理模型的建模算法。
Based-on Dynamic Fuzzy Logic, we proposed a DFL agent logical model and a problem solving model based on multi-agent. 基于动态模糊逻辑(DFL)给出了DFL agent的逻辑模型，建立一个基于多agent的问题求解模型。主要包括以下几个方面的工作：
BVM(Ball Vector Machine)is a faster machine learning algorithm than SVM. 球向量机是一种比SVM更快的机器学习方法。
MAS learning is a domain intercrossed between MAS and machine learning. 多智能体学习是多智能体系统和机器学习等研究领域的交叉。
Quinlan, J.R. Induction of Decision Trees, Machine Learning, 1:81-106, 1986. 胡守仁;余少波;戴葵;(1993).;神经网络导论
This thesis tried to do some researches on reasoning model of DFL, because there were only simple models on the aspect and we wished to enrich the dynamic fuzzy theory system. 因此,本文试图在动态模糊逻辑(DFL)的推理方面做些工作,以进一步丰富动态模糊逻辑(DFL)的内容。
Kernel method is now a powerful alternative in many machine learning tasks. 摘要核方法是机器学习中一种强有力的学习算法。
Due to this,propose default assumption reasoning based on DFL. Here,introduce the reasoning frame,dynamic fuzzy knowledge representation,reasoning algorithms and so on. 因此文中针对研究对象以及它们之间的动态模糊性,提出了基于动态模糊逻辑(DFL)的缺省假设推理,并给出了缺省假设推理的框架描述、动态模糊(DF)知识的表示以及推理算法等。
This paper gives a kind of software Agent model based on Dynamic Fuzzy Logic (DFL) in accordance with characteristics of software Agent, such as Reactivity, Autonomy , Pro-activeness, Evolution, Socialability, and so on. 针对软件Agent的反应性、自治性、自发性、可进化性、社会性等特点,本文基于动态模糊逻辑(DFL),给出了一种软件Agent模型,其内容包括软件Agent的生命周期模型、生理周期模型、学习进化模型、体系结构和协调组织模型等方面。
Do you want to find out how statistics and machine learning can save you time and effort mining text? 你想知道统计学和机器学习在挖掘文本方面能够让你省时省力的原因吗?
Dynamic Fuzzy Logic (DFL) has been a universally focuse of researchers'attention since Li Fanzhang et al, published"A dynamic fuzzy logic system"in 1996. Now there are many research results on DFL. 自1996年李凡长等人发表了“A dynamic fuzzy logic system”以来,动态模糊逻辑(DFL)的研究已被广泛关注,目前已取得了一系列成果。