基于自适应概率神经网络的损伤模式识别研究  

DAMAGE PATTERNS RECOGNITION STUDY BASED ON ADAPTIVE PROBABILISTIC NEURAL NETWORK

作　　者：吴子燕[1] 杨海峰[1] 覃小文[1] 阎云聚[1] 

机构地区：[1]西北工业大学力学与土木建筑学院,西安710072

出　　处：《振动与冲击》2008年第7期8-12,共5页Journal of Vibration and Shock

基　　金：国家863项目(2006AA04Z437);国家自然科学基金(50375123;10472093)

摘　　要：在传统的概率神经网络(PNN)的基础上,提出了通过Gap-Based方法初步估算平滑因子σ,并以遗传算法优化σ参数集的自适应概率神经网络(APNN)模式分类识别方法。以桥梁健康监测委员会提出的两跨桥梁Benchmark模型为例,通过将小波包分解结构在正弦激励和交通激励载荷模型下的动力响应信号的能量特征向量作为网络的输入样本,利用APNN进行了损伤模式进行识别。结果表明,APNN不仅识别精度高和抗噪性能好,而且还能用于输入特征向量参数筛选和降维,提高学习效率和识别精度。An adaptive probabilistic neural netwok (APNN) patterns recognition method is proposed based on the traditional probabilistic neural network (PNN). The ranges of σ parameters are estimated preliminarily by the Gap-Based method and then the genetic algorithm (GA) is adopted to optimize them. In the case study, damage patterns recognition of a two-span bridge benchmark problem proposed by Bridge Health Monitoring Committee is carried out by APNN which selects energy feature vectors of structure dynamic response signal in the sweep sine shaker test and the traffic excitation simulation test as the input variables and uses wavelet packet anslysis for data processing. The results indicate that APNN not only has high identification accuracy and strong noise resistance but also can improve net's learning efficiency by automatic feature selection and dimensionality reduction.

关 键 词：损伤模式识别 自适应概率神经网络(APNN) 能量特征向量 平滑因子 桥梁Benchmark模型 

分 类 号：TU973[建筑科学—结构工程] P315[天文地球—地震学]