Academy & Industry Research Collaboration Center (AIRCC)

Volume 10, Number 07, June 2020

Back-propagation Neural Network based Method for Predicting the Interval Natural Frequencies of Structures with Uncertain-but -bounded Parameters


Pengbo Wang1*, Wenting Jiang2 and Qinghe Shi3, 1Beihang University, China, 2Chinese Academy of Sciences, China and 3Jiangsu University of Technology, China


Uncertain-but-bounded parameters have a significant impact on the natural frequencies of structures, and it is necessary to study their inherent relationship. However, their relationship is generally nonlinear and thus very complicated. Taking advantage of the strong non-linear mapping ability and high computational efficiency of BP neural networks, namely the error back-propagation neural networks, a BP neural network-based method is proposed to predict the interval natural frequencies of structures with uncertain-but-bounded parameters. To demonstrate the proposed method’s feasibility, a numerical example is tested. The lower and upper frequency bounds obtained using the proposed approach are compared with those obtained using the interval-based perturbation method, which is a commonly used method for problems with uncertainties. A Monte Carlo simulation is also conducted because it is always referred to as a reference solution for problems related to uncertainties. It can be observed that as the varying ranges of uncertain parameters become larger, the accuracy of the perturbation method deteriorates remarkably, but the proposed method still maintains a high level of accuracy. This study not only puts forward a novel approach for predicting the interval natural frequencies but also exhibits the broad application prospect of BP neural networks for solving problems with uncertainties.


Back-propagation neural network, Natural frequency, Interval parameter, Perturbation method, Monte Carlo simulation.