非线性回归分析的PSO小波网络方法及应用

文章介绍了粒子群算法（PSO）和小波神经网络的基本原理，把基于粒子群小波网络的混合算法应用到非线性回归问题中，并对算法解决非线性回归问题进行了实践分析，最后建立了测井响应值和物性参数孔隙度之间的回归模型。从仿真结果可以看出，本方法的回归值和岩心分析值符合较好，表明粒子群小波网络进行非线性回归分析是一种有效的数据回归方法。     

Aseismic Reliability Analysis Approach for Offshore Jacket Platform Structures

—By means of nonlinear pushover collapse analysis approach,the aseismic reliability analysesof two offshore jacket platforms in the Bohai Gulf in China are studied according to their ocean locationand environmental loadings there.On the basis of those analyses,an aseismic reliability analysis approachis presented.The results show that the aseismic reliability of those platforms is high.Also it is proved thatthis aseismic reliability analysis approach is simple,practical and reliable.     

SARS疫情预测预报中的分段非线性回归方法

介绍了几种对累计SARS疫情预测预报中的非线性增长曲线模型，说明了Richards增长曲线在这次SARS疫情预测预报中合理性和可行性，由此建立了累计SARS疫情预测预报中的非线性回归点模型。并具体对北京SARS疫情进行了跟踪预测预报，包括整体和分时间段的预测预报，获得了北京SARS疫情随时间的预测预报结果，说明了北京4月底的一系列控制措施对北京SARS疫情所带来的影响，为进一步的后续研究打下了良好基础。     

Understanding the Soil Temperature Variability at Different Depths: Effects of Surface Air Temperature,Snow Cover,and the Soil Memory

The soil temperature(ST)is closely related to the surface air temperature(AT),but their coupling may be affected by other factors.In this study,significant effects of the AT on the underlying ST were found,and the time taken to propagate downward to 320 cm can be up to 10 months.Besides the AT,the ST is also affected by memory effects-namely,its prior thermal conditions.At deeper depth(i.e.,320 cm),the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season.At shallower layers(i.e.,<80 cm),the effects of the AT may be blocked by the snow cover,resulting in a poorly synchronous correlation between the AT and the ST.In northeastern China,this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring.Due to the thermal insulation effect of the snow cover,the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period.These findings may be instructive for better understanding ST variations,as well as land?atmosphere interactions.     

后向平滑与抗差估计融合的SRCKF滤波

将后向平滑平方根容积卡尔曼滤波用于GPS动态单点定位数据处理,并探讨了粗差对后向平滑滤波的影响。借鉴经典卡尔曼滤波抗差估计思想,给出平方根容积卡尔曼滤波的抗差算法以抵抗量测粗差,而当判断不含粗差时使用后向平滑算法,在有效提高滤波精度的同时避免了抗差滤波对每个历元都需进行迭代运算。实测GPS动态数据验证了算法的有效性。     

Nonlinear local Lyapunov exponent and atmospheric predictability research

Because atmosphere itself is a nonlinear system and there exist some problems using the linearized equations to study the initial error growth, in this paper we try to use the error nonlinear growth theory to discuss its evolution, based on which we first put forward a new concept: nonlinear local Lyapunov exponent. It is quite different from the classic Lyapunov exponent because it may characterize the finite time error local average growth and its value depends on the initial condition, initial error, variables, evolution time, temporal and spatial scales. Based on its definition and the at-mospheric features, we provide a reasonable algorithm to the exponent for the experimental data, obtain the atmospheric initial error growth in finite time and gain the maximal prediction time. Lastly, taking 500 hPa height field as example, we discuss the application of the nonlinear local Lyapunov exponent in the study of atmospheric predictability and get some reliable results: atmospheric predictability has a distinct spatial structure. Overall, predictability shows a zonal distribution. Prediction time achieves the maximum over tropics, the second near the regions of Antarctic, it is also longer next to the Arctic and in subtropics and the mid-latitude the predictability is lowest. Particularly speaking, the average prediction time near the equation is 12 days and the maximum is located in the tropical Indian, Indonesia and the neighborhood, tropical eastern Pacific Ocean, on these regions the prediction time is about two weeks. Antarctic has a higher predictability than the neighboring latitudes and the prediction time is about 9 days. This feature is more obvious on Southern Hemispheric summer. In Arctic, the predictability is also higher than the one over mid-high latitudes but it is not pronounced as in Antarctic. Mid-high latitude of both Hemispheres (30°S―60°S, 30°―60°N) have the lowest predictability and the mean prediction time is just 3―4 d. In addition, predictability varies with the seasons. Most regions in the Northern Hemisphere, the predictability in winter is higher than that in summer, especially in the mid-high latitude: North Atlantic, North Pacific and Greenland Island. However in the Southern Hemisphere, near the Antarctic regions (60°S―90°S), the corresponding summer has higher predictability than its winter, while in other areas especially in the latitudes of 30°S―60°S, the prediction does not change obviously with the seasons and the average time is 3―5 d. Both the theoretical and data computation results show that nonlinear local Lyapunov exponent and the nonlinear local error growth really may measure the predictability of the atmospheric variables in different temporal and spatial scales.     

Verification of system identification utilizing shaking table tests of a full‐scale 4‐story steel building

This paper verifies the feasibility of the proposed system identification methods by utilizing shaking table tests of a full‐scale four‐story steel building at E‐Defense in Japan. The natural frequencies, damping ratios and modal shapes are evaluated by single‐input‐four‐output ARX models. These modal parameters are prepared to identify the mass, damping and stiffness matrices when the objective structure is modelled as a four degrees of freedom (4DOF) linear shear building in each horizontal direction. The nonlinearity in stiffness is expressed as a Bouc–Wen hysteretic system when it is modelled as a 4DOF nonlinear shear building. The identified hysteretic curves of all stories are compared to the corresponding experimental results. The simple damage detection is implemented using single‐input‐single‐output ARX models, which require only two measurements in each horizontal direction. The modal parameters are equivalent‐linearly evaluated by the recursive Least Squares Method with a forgetting factor. When the structure is damaged, its natural frequencies decrease, and the corresponding damping ratios increase. The fluctuation of the identified modal properties is the indirect information for damage detection of the structure. Copyright © 2015 John Wiley & Sons, Ltd.     

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION "ON-OFF" SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the on-off switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedur...     

基于非线性插值的地震波旅行时计算

地震波旅行时计算是射线追踪方法的核心问题,其精度直接影响着路径的精度和正演模拟的准确程度。在简要分析目前最典型的几种地震波旅行时计算方法(如有限差分法、线性插值法等)局限性的基础上,提出了一种基于非线性插值的地震波旅行时计算方法,并推导了其计算公式。通过模型的对比试算,不仅验证了所提出的旅行时非线性插值算法的正确性,而且还表明本算法计算旅行时的精度,比Asakawa[5]提出的线性插值算法的精度更高。     

STUDY ON METHOD OF NUMERICAL SIMULATION OF NONLINEAR RANDOM WAVES

This new method can simulate the nonlinear random wavcs processes by computer if the higherorder moments of the probability distribution of the sea surface elevation reflecting the nonlinearity ofthe sea wave are given. Compared with other methods, this method has greater accuracy andflexibility, wider application and faster simulation. Statistical analysis of the sea surface elevationdistribution of the simulated wave process showed obviously the Gram-Charlier series can be used to depictthe distribution of the sea surface elevation.