Inverse Eigenvalue Problems in the Conveyor Based on Surrogate Model

Abstract

The design of the conveyor with dynamic properties can be classified as the solution to inverse eigenvalue problem. In order to deal with non-linear mapping function between structural parameters and mechanical properties. the optimization strategy using BP neural network surrogate model is proposed. The surrogate model is constructed with initial sampling points generated by orthogonal experiment design. A strategy of combining genetic algorithm(GA) and BP neural network was proposed, Optimization solution can be solved using the nonlinear approach capability of BP neural network and the nonlinear search operation of GA by employing the individual fitness value coming from the forecast evaluation based on the BP neural network system to the optimization of a genetic algorithm. which deal with the defects between genetic algorithm and structural reanalysis. This method is effective to structural inverse problems.