Aircraft wing structural damage localization research based on RBF neural network

Abstract

In this article, the wing structural damage is identified and located by using modal analysis and Radial Basis Function (RBF) neural network. The finite element model of an aircraft wing is set up which is used for model analysis. The number of network centers is increased gradually which can ensure that the network has a simplest structure; RBF center is determined by K-means clustering algorithm which can improve the representative of each center and improve the training accuracy; the network weights is determined using the concept of pseudo inverse matrix and inverse matrix, which can shorten the training period and improve training efficiency. The computer simulation result shows that this damage identification method has high identification accuracy. The relative error is 1.422%, and the absolute error is 31.28mm. Comparing with the analyzing spar and skin individually, this method has a more spreading value.

Bee-inspired RBF network for volume estimation of individual trees

“…where n is the amount of centers and d max is the biggest Euclidean distance between all centers (Bao et al, 2011). The heuristic that received the name of average define the spread of each function as the average of Euclidean distances between the current center to all other centers (Eq.…”