Damage Identification in Timber Bridges Utilising the Damage Index Method and Neural Network Ensembles

2009 ◽  
Vol 9 (3) ◽  
pp. 181-194 ◽  
Author(s):  
U Dackermann ◽  
J Li ◽  
B Samali
Keyword(s):  
Neural Network ◽  
Damage Identification ◽  
Damage Index ◽  
Index Method ◽  
Neural Network Ensembles ◽  
Network Ensembles ◽  
Timber Bridges

Damage Identification Using Wavelet Packet Transform and Neural Network Ensembles

2018 ◽  
Vol 18 (12) ◽  
pp. 1850148 ◽  
Author(s):  
Xiang Zhang ◽  
Renwen Chen ◽  
Qinbang Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Damage Identification ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Rotor Blades ◽  
Identification Accuracy ◽  
Vibration Responses ◽  
Neural Network Ensembles ◽  
Network Ensembles

This study presents a damage identification method that combines wavelet packet transforms (WPTs) with neural network ensembles (NNEs). The WPT is used to extract damage features, which are taken as the input vectors in the NNEs used for damage identification. An experiment was performed on a helicopter rotor blades structure to verify the proposed method. First, the vibration responses collected by different sensors are decomposed using the WPT. Second, the relative band energy of each decomposed frequency band is calculated and fused as the damage feature vectors. Third, two types of the NNEs are designed. One is based on the backward propagation neural networks (BPNNs) for detecting the damage locations and severities and the other one is based on the probabilistic neural network (PNN) to detect the damage types. Finally, the trained NNEs are employed in damage identification. From the identification outcomes, it is concluded that damage information can be extracted effectively by the WPT and the identification accuracy of the NNEs is better than that of individual neural networks (INNs).

scholarly journals Deep Convolutional Neural Network Ensembles Using ECOC

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sara Atito Ali Ahmed ◽  
Cemre Zor ◽  
Muhammad Awais ◽  
Berrin Yanikoglu ◽  
Josef Kittler
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Neural Network Ensembles ◽  
Network Ensembles

scholarly journals Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data

2020 ◽  
Vol 7 (02) ◽  
pp. 1 ◽  
Author(s):  
Rahul Paul ◽  
Matthew B. Schabath ◽  
Robert Gillies ◽  
Lawrence O. Hall ◽  
Dmitry B. Goldgof
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clinical Data ◽  
Lung Nodule ◽  
Hybrid Models ◽  
Neural Network Ensembles ◽  
Network Ensembles
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