Performance analysis of simplified Fuzzy ARTMAP and Probabilistic Neural Networks for identifying structural damage growth

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• 作者：Mario A. de Oliveira^a ; ^{mario.oliveira@cba.ifmt.edu.br} ; ^{mandersoneee@gmail.com} ; Daniel J. Inman^b ; ^{daninman@umich.edu}
• 关键词：SHM ; Electromechanical impedance ; PZT ; Damage assessment ; Composite materials
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：53-63
• 全文大小：3710 K
• 卷排序：52

文摘

An unprecedented comparative performance analysis between Fuzzy ARTMAP and Probabilistic Neural networks applied to identifying structural damage. The fuzzy ARTMAP method is better suited to the problem of damage growth in regard to success and training/testing times. The suitable damage prognosis method can lead to an increase in safety and reduce maintenance costs in a variety of applications. It contributes to helping researchers choose more effective approaches for structural damage growth.