TY - JOUR
TI - Correlation analysis based on neural network copula function
AU - Li Haibin
AU - Sun Lijun
AU - Yao Qinghu
JN - Thermal Science
PY - 2023
VL - 27
IS - 3
SP - 2081
EP - 2089
PT - Article
AB - The joint-distribution function between variables plays an important role in  reliability analysis. A method is proposed for constructing the function  using a neural network, which is used to construct a copula model under  arbitrarily measured data, including the input and output values of the  neural network using an empirical cumulative distribution. Three traditional  copula function models are constructed based on the Kendall rank-correlation  coefficients. Based on the Euclidean distance method, the neural network  copula and three copula function models are compared.