一种基于奇异值分解技术的模型定阶方法

摘要：模态参数识别中模型阶次的确定非常重要。基于奇异值分解技术，探讨模型阶次的确定方法，提出了利用奇异值的相对变化率来确定模型的阶次。该方法利用结构的量测脉冲响应信号构造Hankel矩阵，对其进行奇异值分解后计算奇异值的相对变化率，变化率最大的地方对应着模型的阶次。通过数值算例研究了噪声因素对模型阶次确定的影响，并利用模型实验数据验证了本方法的有效性。

Model order determination based on singular value decomposition

Model order determination is essentially important for modal parameters identification.An index,named as model order indicator,was proposed to determine the proper model order.In the method,the impulse response function was utilized to construct the Hankel matrix and then the singular value decomposition(SVD) was used to obtain the singular values.The index proposed was defined as the relative change ratio of the decreasing singular values and the maximum change ratio corresponds to the model order.Different cases,including various noise levels with different data length,were investigated.The effectiveness of the proposed method was verified by using measured signals from model test.