抗离群值的鲁棒正则化贯序超限学习机

针对离群值环境下的在线学习问题,提出一种鲁棒正则化贯序超限学习机(Robust regularized online sequential extreme learning machine,RR-OSELM)。RR-OSELM以增量学习新样本的方式实现在线学习,并在学习过程中基于样本的先验误差进行逆向加权计算以降低学习模型对于离群值的敏感性;同时RR-OSELM通过融合使用Tikhonov正则化技术进一步增强了其在实际应用中的稳定性。实验结果表明,RR-OSELM具有较同类算法更好的鲁棒性和实用性,对于离群值环境下的在线建模与预测问题是积极有效的。     

用于雷达方位超分辨的约束迭代Tikhonov正则化算法

针对Tikhonov正则化算法噪声适应能力差和不便于引进额外的约束对解进行限制等缺点,利用迭代Tik-honov正则化算法对雷达方位超分辨进行研究,证明了迭代Tikhonov正则化反卷积公式的收敛性,分析了迭代Tik-honov正则化算法的频域性质,在存在噪声的情况下,提出相应的噪声抑制方法,得到约束迭代Tikhonov正则化算法。针对不同信噪比情况进行了计算机仿真实验,结果表明,与迭代Tikhonov正则化算法相比,约束迭代Tikhonov正则化算法具有较强的噪声适应能力,与常用的约束迭代方法（CID）算法相比,具有较快的收敛速度,初步验证了算法的有效性。     

Multi-input multi-output random vibration control using Tikhonov filter

Noises always disturb the control effect of an environment test especially in multi-input multi-output (MIMO) systems. If the frequency response function matrices are ill-conditioned, the noises in the driving forces will be amplified and the response spectral lines may awfully exceed their tolerances. Most of the major biases between the response spectra and the reference spectra are pro-duced by the amplified noises. However, ordinary control algorithms can hardly reduce the level of noises. The influences of the noises on both the auto-and cross-power spectra are analyzed in this paper. As a conventional frequency domain method on the inverse problem, the Tikhonov filter is adopted in the environment test to suppress the exceeding spectral lines. By altering regularization parameters gradually, the auto-power spectra can be improved in a closed control loop. Instead of using the traditional way of selecting regularization parameters, we observe the coherence change to estimate noise eliminations. Incidentally, the requirement of coherence control can be realized. The errors of the phase are then studied and a phase control algorithm is introduced at the end as a sup-plement of cross-power spectra control. The Tikhonov filter and the proposed phase control algo-rithm are tested numerically and experimentally. The results show that the noises in the vicinity of lightly damped resonant peaks are more stubborn. The response spectra are able to be greatly improved by the combination of these two methods.     

基于应变测量的结构载荷分布反演方法

针对基于应变测量的载荷分布反演,首先对比分析现有几种影响系数法,并讨论影响载荷反演精度的几个关键问题。为解决单位载荷工况选取的难题,提出一种基于施密特正交化的最大垂直距离逐步筛选基工况法,从设计载荷工况库中筛选载荷基工况和应变基工况,建立一套完整的工程可行的载荷分布反演流程。为解决反向矩阵病态化的难题,引入Tikhonov正则化方法。以某型飞机机翼疲劳试验作动筒载荷和光纤传感器数据为算例,假设3种应变测量误差情况（无误差、5%随机误差、10%随机误差）,验证了提出的载荷分布反演方法具有很高的预测精度和鲁棒性,可为新一代航空航天器的结构健康监控提供一条可靠的载荷识别途径。     

基于波叠加方法的可视化声源识别

介绍了一种基于波叠加方法的可视化声源识别技术。应用波叠加基本理论,采用Burton-Miller型组合层势法或复数失径方法克服解的非惟一性问题,并通过Tikhonov正则化方法求解这一反问题。对两脉动球声源和两摆动球声源进行数值仿真,并与解析解进行对比,仿真结果可以很好地识别出两声源的位置。另外,稳健性分析结果表明该方法具有较好的稳健性。最后,通过实验进一步验证了波叠加方法用于声源识别的可行性。     

Application of hybrid regularization method for tomographic reconstruction of midlatitude ionospheric electron density

Reconstructing ionospheric electron density (IED) is an ill-posed inverse problem, with classical Tikhonov regularization tending to smooth IED structures. By contrast, total variation (TV) regularization effectively resists noise and preserves discontinuities of the IED. In this paper, we regularize the inverse problem by incorporating both Tikhonov and TV regularization. A specific formulation of the proposed method, called hybrid regularization, is introduced and investigated. The method is then tested using simulated data for the actual positions of the GPS satellites and ground receivers, and also applied to the analysis of real observation data under quiescent and disturbed ionospheric conditions. Experiments demonstrate the effectiveness, and illustrate the validity and reliability of the proposed method.