Dual maximum entropy probability density function model and optimization

Based on high nonlinearity, hard convergence, low computational efficiency in the computational process by the classic maximum entropy probability density function model, a method called dual maximum entropy probability density function model+sequential updating method was proposed. Because of the unsteady optimization routine, a transformation formula of Lagrangian coefficient was rededuced. The probability density function and reliability of several common distributions and one complex distribution was calculated by classic and dual maximum entropy probability density function models. This shows that compared with classic maximum entropy probability density function model, the optimization function of dual maximum entropy probability density function model has advantages of low nonlinearity and simple form, the sequential updating method not only overcomes the sensitivity problem of initial value but exhibits high computational efficiency; dual maximum entropy probability density function model+sequential updating method has the highest computational accuracy, and can be well applied to the reliability problems of complex distribution.