The Proceedings of the International Conference on Motion and Vibration Control
Online ISSN : 2424-2977
6.2
Conference information
DEFECT IDENTIFICATION USING LEARNING VECTOR QUANTIZATION NEURAL NETWORK
Wakae KozukueHideyuki Miyaji
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CONFERENCE PROCEEDINGS FREE ACCESS

Pages 1181-1184

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Abstract
The Learning Vector Quantization (LVQ) neural network is applied to the defect identification problem for structures, which is important when constructing the mathematical model of structures. In this study the eigenmodes of a plate obtained from FEM and the location of a defect contained in that plate are used as the training deta for neural network and the position of the defect is identified by giving the unlearned input data to the trained network. As a result the better accuracy is obtained compared to the case when using the backpropagation neural network commonly used in the various studies
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© 2002 The Japan Society of Mechanical Engineers
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