The hybrid uncertain neural network method for mechanical reliability analysis

Peng, Wensheng;Zhang, Jianguo;You, Lingfei

  • Received : 2015.06.04
  • Accepted : 2015.12.04
  • Published : 2015.12.30


Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.


neural network;interval variables;hybrid uncertainty model;aerospace mechanism;nonlinearity reliability


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