基于LS-SVM的模态参数识别方法

 最小二乘支持向量机回归用于系统的模态参数识别研究。针对经典的最小二乘支持向量回归缺少鲁棒性和稀疏性的缺陷,提出了一种兼具鲁棒性和稀疏性的最小二乘支持向量回归的算法,并保持了它原有的计算速度快的优点。最后,结合结构动力学方程的自回归滑动平均时间序列形式,给出了结构的模态参数提取方法和流程,给出了相应的数值算例以及进行了实验的检验证明。结果表明,本文的方法能够快速、准确地提取出系统的模态参数。     

基于压缩感知的频率和DOA联合估计算法

波达方向（DOA）估计是阵列信号处理的一个重要问题,针对信号的DOA估计问题,本文基于压缩感知理论,提出了一种新的频率和角度联合估计算法。首先利用方向波数的空间稀疏性,建立过完备稀疏字典,然后利用压缩采样阵列结构通过求解最优化问题得到方向波数的高分辨估计,最后利用最优空域滤波实现信号到达角度和频率的配对。相对于传统算法,该算法能够实现多信号DOA的高分辨估计,且经过压缩采样后降低了运算量,仿真验证了该算法的正确性与有效性。     

Improved Scheme for Fast Approximation to Least Squares Support Vector Regression

The solution of normal least squares support vector regression（LSSVR）is lack of sparseness,which limits the real-time and hampers the wide applications to a certain degree.To overcome this obstacle,a scheme,named I2FSA-LSSVR,is proposed.Compared with the previously approximate algorithms,it not only adopts the partial reduction strategy but considers the influence between the previously selected support vectors and the willselected support vector during the process of computing the supporting weights.As a result,I2FSA-LSSVR reduces the number of support vectors and enhances the real-time.To confirm the feasibility and effectiveness of the proposed algorithm,experiments on benchmark data sets are conducted,whose results support the presented I2FSA-LSSVR.