A pattern recognition of the gas fluid distrbution in blast furnace is developed by the combination of statistical approach with neural networks 将模式识别中的统计方法与人工神经元网络方法有机地结合起来,设计了一种综合的高炉煤气流分布模式识别系统。
( l ) we carry out the research on the characteristic property of geometric distribution . hence , we propose the two new concepts : statistical closed property and statistical approach property ( 1 )研究了几何分布的特征性质,首次提出统计封闭性和统计贴近性的概念。
Abstract : a pattern recognition of the gas fluid distrbution in blast furnace is developed by the combination of statistical approach with neural networks 文摘:将模式识别中的统计方法与人工神经元网络方法有机地结合起来,设计了一种综合的高炉煤气流分布模式识别系统。
Compared with classical statistical approaches , neural networks approach to pattern recognition have many advantages , such as self - adaptability , parallel processing , robustness and strong classification ability 同时,与传统统计模式识别方法相比,利用人工神经网络方法进行模式识别具有自适应、并行性、鲁棒性、分类能力强等优势。
It masquerades as the email server ' s local delivery agent and filters / learns spam using a bayesian statistical approach which provides an administratively maintenance - free , self - learning anti - spam service 它作为邮件服务器的本地发送代理,提供使用贝叶斯统计方法的垃圾邮件过滤/学习,这种方法提供免维护的管理服务和自学习的反垃圾邮件服务。
Using its probability statistical approach , this paper analyzes the uncertainty distribution of net pay thickness and permeability in the reservoir , researches the impart of geologic parameter uncertainty on gas well binomial productivity equation 应用概率统计法,分析了储集层有效厚度、渗透率的不确定性分布,研究了地质参数不确定性对气井二项式产能方程的影响。
Conception hierarchy tree classifiers which is a statistical approach have played an important role in attribute - oriented induction . it can help us discover the characteristics of data , make them more understandable and organized in concept - oriented structure 通过它对数据库中的数据进行分类可以帮助我们发现数据的特征,以更加容易理解的方式总结数据,并且依据面向概念的结构来组织数据。
Owing to the problem of causality diagram don ’ t include self - study mechanism at present and the prior knowledge of reasoning is supplied completely by field experts , some methods of learning causality diagram ’ s parameters and structure with statistical approach is presented 针对目前因果图不包括自学习机制、推理的先验知识完全由领域专家提供的问题,提出了利用已有数据学习因果图结构与参数的方法。