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模糊主成分分析
相关语句
  fuzzy principal component analysis
     Research and Analysis of Fuzzy Principal Component Analysis
     模糊主成分分析方法的研究与分析
短句来源
     Appraisement and selection decision of suppliers based on the method of fuzzy principal component analysis
     基于模糊主成分分析法的供应商评价与选择决策
短句来源
  “模糊主成分分析”译为未确定词的双语例句
     As to this problem,this paper puts forward the concepts of fuzzy expectation,fuzzy deviation,fuzzy variance,fuzzy covariance and fuzzy correlation,then introduces a powerful approach to improve the PCA(fuzzy PCA).
     针对经典主成分分析的缺点本文提出了模糊数学期望、模糊离差、模糊方差、模糊协方差及模糊相关系数的概念,从而提出了一种有效方法来改进经典主成分分析,即模糊主成分分析(Fuzzy PCA)。
短句来源
     Secondly, after a detailed comparative analysis of several existing tender evaluation methods, I propose a multicriteria synthetic method based on fuzzy analytic hierarchy process and principal components analysis (PCA), which not only improves traditional PCA, but also integrates the subjective judgments into objective data analysis to remedy the deficiencies of those evaluation method only relied on subjective or objective judgment.
     其次,在分析、比较了现有评标方法的适用性及局限性的基础上,提出了创新的多指标综合评标方法——基于模糊主成分分析的评标方法及其模型,该方法将模糊层次分析融入到了主成分分析中,使得评标结果既能体现出评标委员会的主观判断,又能体现出客观指标数据信息,实现了主观评价与客观评价相结合。
短句来源
  相似匹配句对
     Research and Analysis of Fuzzy Principal Component Analysis
     模糊主成分分析方法的研究与分析
短句来源
     Theory of Fuzzy Systems
     模糊系统论
短句来源
     T-Fuzzy Subrings
     T-模糊子环
短句来源
     PCA and algorithms analysis
     主成分分析及算法
短句来源
     Appraisement and selection decision of suppliers based on the method of fuzzy principal component analysis
     基于模糊主成分分析法的供应商评价与选择决策
短句来源
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  fuzzy principal component analysis
Fuzzy principal component analysis and its Kernel-based model
      
Based on the theory of fuzzy sets, this paper presents Fuzzy Principal Component Analysis (FPCA) and its nonlinear extension model, i.e., Kernel-based Fuzzy Principal Component Analysis (KFPCA).
      
Two fuzzy principal component analysis (FPCA) methods for robust estimation of principal components were applied and compared with classical PCA.
      
For example, a three component model, fuzzy principal component analysis-first component (FPCA-1) accounts for 62.37% of the total variance and fuzzy principal component analysis-orthogonal (FPCA-o) 90.11%; PCA accounts only for 58.30%.
      


When there are too many indexes of suppliers' selection and it is not easy to make a decision.On the one hand it is easy to find the principal components,their contribution rate(the early weight) and reduce the dimension with the method of principal component analysis.One the other hand it can be used to modify the early weight and make the weight rationally.The appraising value endow with new weight are scientific and impersonal.They can be used as reference of suppliers selection.

企业供应商的选择所设计的指标非常多时,就很难轻易地作出决策.采用模糊主成分分析法一方面可以容易地从中找出主要因素及其贡献率(初始权重),达到降维的目的,另一方面可以对主成分初始权重进行修正使得所得到的权重合理,赋予新的权重后使得评价值比较科学和客观,能够作为进行供应商的参考.

Principal component analysis(PCA) is a favorite multivariate statistical method for data compression and information extraction.However,it is well known that PCA is sensitive to outliers and missing data,but it maybe get deformity and error result.As to this problem,this paper puts forward the concepts of fuzzy expectation,fuzzy deviation,fuzzy variance,fuzzy covariance and fuzzy correlation,then introduces a powerful approach to improve the PCA(fuzzy PCA).It can be explained that if fuzzy math is applied into...

Principal component analysis(PCA) is a favorite multivariate statistical method for data compression and information extraction.However,it is well known that PCA is sensitive to outliers and missing data,but it maybe get deformity and error result.As to this problem,this paper puts forward the concepts of fuzzy expectation,fuzzy deviation,fuzzy variance,fuzzy covariance and fuzzy correlation,then introduces a powerful approach to improve the PCA(fuzzy PCA).It can be explained that if fuzzy math is applied into PCA by making fuzzy sets to participate in decision-making,it can raise accuracy and reliability of the decision results.At the same time,this paper establishes a data analysis platform that has transplant ability and offers general interfaces to other uses;it has offered maximum convenience to solve similar problems.

主成分分析(PCA)是一种广泛应用于数据压缩的多元统计分析方法。然而,经典主成分分析它对极端值及缺失值非常的敏感,而极端值与缺失数据会带来残缺或错误的分析结果。针对经典主成分分析的缺点本文提出了模糊数学期望、模糊离差、模糊方差、模糊协方差及模糊相关系数的概念,从而提出了一种有效方法来改进经典主成分分析,即模糊主成分分析(Fuzzy PCA)。把模糊数学的知识应用到主成分分析中,使模糊集参与决策分析,使人为因素带来的不确定性达到最小,从而大大提高了分析结果的准确性和可信度。同时,本文以模糊主成分分析为出发点,建立了一个数据分析平台,平台具有可移植性,为其它使用提供了通用接口,为解决类似问题提供极大方便。

 
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