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2020 | nr 1 (44) | 173--205
Tytuł artykułu

Improved AHP and Manifold Learning Model for R&D And Transformation Functional Platform Performance Evaluation

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The accurate assessment of R&D and transformation functional platform performance is an important basis for the improvement of high-tech industry competitiveness. In this paper, the authors establish an improved AHP-manifold learning model to solve the problems of the traditional AHP method which needs to satisfy the consistency condition in constructing judgment matrices. In the ranking process of inconsistency of judgment matrices, on the basis of the neighbour distance, the neighbour distance matrices of the data sets corresponding to judgment matrices are constructed first. Next, each data point is mapped to a low-dimensional global coordinate system based on the linear representations of the neighbour points, and the low-dimensional embeddings corresponding to the judgment matrices are obtained. Then the ranking conclusion is obtained by analysing the superiority and inferiority ranking of the elements according to the correspondingly calculated low-dimensional embeddings from each hierarchy. Finally, the proposed method and another numerical method are used to assess R&D and transformation functional platform performance. The result illustrates that the proposed method has a higher level of effectiveness and practicability, and it can provide good guidance for improving platform performance.(original abstract)
Rocznik
Numer
Strony
173--205
Opis fizyczny
Twórcy
autor
  • School of Management, Shanghai University, Shanghai, China
autor
  • School of Economics &Management, Tongji University, Shanghai, China
  • Institute for Manufacturing, University of Cambridge, Cambridge, UK
autor
  • School of Economics and Trade, Shanghai Urban Construction Vocational College, Shanghai, China
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Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171597083

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