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.
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