Pulse transiently chaotic neural network for the analysis method of reliability

The structural reliability index β can be solved as an optimization problem in engineering design. However, when the limited state function is high nonlinearity with concave failure domain, the classic methods of solving nonlinear programming such as sequential quadratic programming (SQP) method, penalty function method, and gradient projection method etc, have disadvantages in solving the local minimum solution. It is still difficult to deal with how to solve local minimum solution taking into account the calculation accuracy and efficiency. This paper presents a new method of reliability that the problem for solving reliability indexes is transformed into the nonlinear programming problem with constraint condition. Penalty function method is used to convert the problem into unconstrained nonlinear programming one. Pulse transiently chaotic neural network (PTCNN) is introduced to optimize globally so as to solve constrained nonlinear programming problems with local minimum solution quickly and efficiently. Finally the examples of different types of non-linear limit state functions are presented to prove that this method is feasible and efficient to address the problem with high dimension, highly nonlinear, non-differentiable and non-convex failure domain.