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基于自适应共振理论的结构损伤识别
引用本文:焦莉,李宏男.基于自适应共振理论的结构损伤识别[J].沈阳建筑工程学院学报(自然科学版),2005,21(5):486-490.
作者姓名:焦莉  李宏男
作者单位:[1]沈阳建筑大学理学院,辽宁沈阳110168 [2]大连理工大学海岸及近海工程国家重点实验室,辽宁大连116024
基金项目:国家自然科学基金项目(50408031),辽宁省科学技术重点基金项目(20042149),辽宁省科学技术基金项目(20032120)
摘    要:目的基于自适应共振理论,提出一种基于ART2神经网络的结构损伤识别方法,以实现结构损伤识别的自主学习.方法采用一种改进算法来解决ART2方法中对输入矢量必须是非负实数的要求,并通过主成分分析方法对网络的输入矢量进行降维处理.结果通过对健康监测基准问题模型的计算表明,所采用的改进算法使得网络的输入扩展到整个实数域,且主成分分析方法有效地降低了输入矢量的维数,减少了网络的学习训练时间,从而提高了网络的泛化和判别决策能力.结论基于ART2神经网络的结构损伤识别方法具有自组织、反馈式增量学习机能,能够在不破坏原有记忆样本的情况下,学习新的样本,可以在较强噪声环境下快速准确地识别损伤,适宜于结构损伤的在线监测.

关 键 词:自适应共振理论  神经网络  主成分分析  损伤识别  基准结构
文章编号:1671-2021(2005)05-0486-05
修稿时间:2005年3月20日

Damage Identification of Structures Based on Adaptive Resonance Theory
JIAO Li.Damage Identification of Structures Based on Adaptive Resonance Theory[J].Journal of Shenyang Archit Civil Eng Univ: Nat Sci,2005,21(5):486-490.
Authors:JIAO Li
Abstract:An ART2 neural network based on adaptive resonance theory is put forward in this work to identify the damage of the structures and to realize the on-line self-study of the network.An improved algorithm and the principal components analysis method are adopted into the network to improve its ability in damage identification of the structures.In order to validate the effectiveness of this method,the numerical sample about the benchmark problem is analyzed by using the network under different noisy conditions.The results show that the improved ART2 algorithm can expand the input vectors from the nonnegative real number field of the standard ART2 to the whole real number field,which can broaden the damage identification scope of ART2.The dimensions of the input vectors are reduced by the principal components analysis,which can effectively shorten the training time of the network.It can be concluded from the numerical analyses that the improved ART2 neural network can be used to study the new samples itself without destructing the original samples.It can recognize the damage patterns under strong noisy environments rapidly and precisely,and it is also applicable to on-line monitoring.
Keywords:adaptive resonance theory  neural network  principal components analysis  damage identification  benchmark structure
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