Open AccessEvaluation of Systems Current Status by PCA-RBF Neural Network and Novel Fuzzy Intelligence Method
Author(s) -
Zimu Li,
Lan Luo,
Yuqing Lu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1982/1/012042
Subject(s) - principal component analysis , prosperity , artificial neural network , artificial intelligence , radial basis function , computer science , fuzzy logic , transformation (genetics) , range (aeronautics) , dimension (graph theory) , computational intelligence , function (biology) , machine learning , data mining , pattern recognition (psychology) , mathematics , engineering , economic growth , biochemistry , chemistry , aerospace engineering , pure mathematics , economics , gene , evolutionary biology , biology
The health and the sustainability of a higher education system are crucial to the prosperity of a nation. It is crucial to effectively evaluate a higher education system in various aspects and promptly adjust the corresponding educational policies. Firstly, 23 higher education quality indicators with respect to more than 1,000 universities are carefully collected and converted to the corresponding indicators of 40 countries/regions. Moreover, such indicators are normalized by the range transformation method in order to facilitate subsequent analyses. Secondly, a novel fuzzy intelligence (F&I) method is proposed to model the health states of different higher education systems based on radial basis function (RBF) neural networks. This method firstly reduces the input indicators’ dimension by the principal component analysis (PCA) technique. Using the PCA results as the input layer, the RBF neural network is carefully trained and the F&I score of each country/region is therefore obtained. Next, the hierarchical cluster analysis is carried out to depict the health state of each county/region. 1, 5, 5 and 29 countries/regions are categorized as healthy, good, general and unhealthy, respectively.