| 基于FFT和神经网络的复模态参数识别 |
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| 引用本文： | 刘海源,陈建功,张永兴. 基于FFT和神经网络的复模态参数识别[J]. 东南大学学报(自然科学版), 2009, 0(Z2) |
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| 作者姓名： | 刘海源 陈建功 张永兴 |
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| 作者单位： | 重庆大学土木工程学院; |
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| 基金项目： | 国家杰出青年科学基金资助项目(50625824); 国家自然科学基金资助项目(50679097) |
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| 摘 要： | 为了精确识别结构复模态参数,提出了一种基于快速傅里叶变换(FFT)和人工神经网络的模态识别方法.该方法首先对自由振动信号进行FFT预处理,得到粗略的各阶模态频率和相位.然后,根据模态的阶数设定神经元的个数,根据预处理后得到的频率和相位设定神经网络权值和基函数参数迭代的初始值.最后,通过对人工神经网络进行训练,达到利用自由振动信号进行时域模态识别的目的.仿真结果表明,该算法可消除频率法识别中因频谱泄露与噪声等产生的误差,提高模态识别的精度,因而是一种有效的时域识别方法.
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| 关 键 词： | 复模态 参数识别 快速傅里叶变换 神经网络 参数可调的基函数 |
Identification of complex modal parameters based on FFT and neural network |
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| Liu Haiyuan Chen Jiangong Zhang Yongxing. Identification of complex modal parameters based on FFT and neural network[J]. Journal of Southeast University(Natural Science Edition), 2009, 0(Z2) |
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| Authors: | Liu Haiyuan Chen Jiangong Zhang Yongxing |
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| Affiliation: | Liu Haiyuan Chen Jiangong Zhang Yongxing (School of Civil Engineering,Chongqing University,Chongqing 400045,China) |
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| Abstract: | To precisely identify complex modal parameters of structural systems,a model identification algorithm is presented based on the fast Fourier transform(FFT) and the artificial neural network model.First,the free vibration signal is preprocessed by the FFT algorithm.The frequency and the phase of all modals are obtained.Secondly,the number of neural nodes is determined by the orders of the modals.The initial weights of neural network and the iterative initial parameters of the base function are assigned accor... |
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| Keywords: | complex modal parameter identification fast Fourier transform neural network base function with adjusted parameters |
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