Correlation analysis based on neural network copula function

The joint-distribution function between variables plays an important role in reliability analysis. A method is proposed for constructing the using a neural network, which used to construct copula model under arbitrarily measured data, including input and output values of network empirical cumulative distribution. Three traditional models are constructed based on Kendall rank-correlation coefficients. Based Euclidean distance method, three compared.