斜拉索大桥的稀疏复指数与基频识别法
发布时间：2019-01-07 19:37
【摘要】：针对复指数法(CE)需要准确定阶且对噪声敏感的问题,提出一种稀疏改进的复指数算法。先用高阶次的拟合模型进行模态计算,然后用稀疏优化法代替最小二乘法计算振型系数,从众多振型系数中自动选出真实模态对应的系数,最终达到剔除虚假模态的目的。通过引入稀疏优化方法解决了使用高阶拟合模型难以有效剔除虚假模态的问题,突破了模型阶次不能过高的限制,提高了算法的精度和抗噪声性能,令其在斜拉索基频测试中表现出更高的识别精度。
[Abstract]:Aiming at the problem that complex exponential method (CE) needs to be quasi-deterministic and sensitive to noise, a sparse and improved complex exponential algorithm is proposed. First, the modal calculation is carried out with the fitting model of higher order, then the modal coefficient is calculated by sparse optimization method instead of least square method, and the corresponding coefficients of real modes are automatically selected from many modal coefficients, finally the false modes are eliminated. The sparse optimization method is introduced to solve the problem that it is difficult to eliminate the false modal effectively by using the high-order fitting model, which breaks through the limitation that the model order can not be too high, and improves the accuracy and anti-noise performance of the algorithm. It can show higher recognition accuracy in the basic frequency measurement of stay cables.
【作者单位】： 宁波大学信息科学与工程学院;
【基金】：国家自然科学基金资助项目(61071198) 浙江省自然科学基金资助项目(LY13F0110015) 宁波市自然科学基金资助项目(2012A610019)
【分类号】：TN911.7
[Abstract]:Aiming at the problem that complex exponential method (CE) needs to be quasi-deterministic and sensitive to noise, a sparse and improved complex exponential algorithm is proposed. First, the modal calculation is carried out with the fitting model of higher order, then the modal coefficient is calculated by sparse optimization method instead of least square method, and the corresponding coefficients of real modes are automatically selected from many modal coefficients, finally the false modes are eliminated. The sparse optimization method is introduced to solve the problem that it is difficult to eliminate the false modal effectively by using the high-order fitting model, which breaks through the limitation that the model order can not be too high, and improves the accuracy and anti-noise performance of the algorithm. It can show higher recognition accuracy in the basic frequency measurement of stay cables.
【作者单位】： 宁波大学信息科学与工程学院;
【基金】：国家自然科学基金资助项目(61071198) 浙江省自然科学基金资助项目(LY13F0110015) 宁波市自然科学基金资助项目(2012A610019)
【分类号】：TN911.7
【参考文献】
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