应用粒子群算法优化地位指数曲线模型参数研究

以优势高和地位指数的估测误差最小为目标函数,采用粒子群优化算法求解地位指数曲线模型的参数.结合实例与免疫算法比较,结果表明:粒子群优化算法求解的参数使模型的总体误差更小,精度更高,拟合效果更理想,更加科学合理,同时也提高了幼林的估算精度.研究的结果为森林经营中生长模型参数的求解以及相关研究提供了新的应用思路,也拓宽了粒子群优化算法在林业科学中的应用.     

基于粒子群优化的投影寻踪聚类模型及其应用

投影寻踪聚类分析是根据设计的投影指标函数,并在相关约束条件下进行问题优化分析的过程.给出了用于求解投影指标函数的粒子群算法,并将构造的模型应用于森林承载力评价.仿真实验结果表明:与基于遗传算法优化的模型比较,基于粒子群优化的模型简单、容易实现并且没有许多参数需要调整;在应用上,基于粒子群优化的模型可获得更优的解,并可预计模型在森林承载力评价中具有重要的应用价值.     

基于蚁群算法的暴雨强度公式的优化

为使蚁群算法能用于复杂的连续函数的参数优化问题,需对离散优化的蚁群算法思想进一步加以引申和发展,提出了一种基于蚁群算法的函数优化新模型,并将其应用于暴雨强度公式中的参数优化。新的优化算法与其它优化算法结果的比较表明:该算法应用于函数优化问题,精度较高,有较强的可行性、并行性和实用性。     

SCE-UA算法优化土壤湿度方程中参数的性能研究

借助于一维土壤湿度模型,分别将土壤成份和土壤性质相关参数作为待优化的参数,通过观测系统模拟试验的方式,评估SCE-UA (Shuffled Complex Evolution Algorithm) 优化算法对这些参数的优化效果。结果表明:优化的效果不仅依赖于参数的取值范围,还依赖于参数的敏感性,敏感的参数通过优化算法易得到最优值;不敏感的参数存在“不敏感区间”,在“不敏感区间”中易陷入次优,通过缩小参数优化分布区间和增加优化的次数可以部分提高优化的效果。此外,模型的超定性也可能导致参数次优值的出现,而通过恰当地给出参数之间的约束条件和优化判据,可以提高参数优化的效果。     

作物模型参数敏感性分析现状与展望

作物模型作为作物产量预测等方面应用广泛、作用巨大的重要工具,其参数的校正和优化等工作是模型模拟的前提。普通的试错法缺乏客观性且效率低下,对作物模型的输入参数进行敏感性分析,识别模拟过程中参数的敏感程度,能够有效识别关键参数并减少率定的参数量,为后续模型参数优化和校正工作奠定基础。本文主要分析了作物模型的局部敏感性分析方法和全局敏感性方法的利弊及其适应条件,涵盖傅立叶幅度灵敏度检验法(FAST)、Morris法、LH-OAT法、普适似然不确定性估计法(GLUE)、Sobol法以及扩展傅立叶幅度灵敏度检验法(EFAST)等,回顾各方法在作物模型中的研究现状,并对作物模型参数敏感性分析方法提出了展望。     

基于改进粒子群算法的机器人几何参数标定研究

为了提高机器人末端绝对定位精度,提出了基于改进粒子群算法（IPSO）的机器人几何参数标定方法.首先,为避免当机器人相邻两轴线平行或接近平行时,模型存在奇异性,建立了串联机器人MDH模型;其次,针对机器人几何参数标定特点,提出用改进粒子群算法优化标定机器人几何参数,其中粒子初始位置和速度由拟随机Halton序列产生,采用浓缩因子法修改粒子飞行速度,建立了用IPSO标定机器人几何参数目标函数数学模型,确立了用该算法优化标定几何参数的具体步骤.通过对ER10L-C10工业机器人仿真与实测标定,结果证实：采用该方法能够快速标定机器人几何参数,经标定后的机器人末端绝对定位精度有大幅提高.该算法简单,鲁棒性强,易于在工业机器人标定中推广应用.     

粒子群算法在陆面过程模式参数优化中的应用研究

陆面过程模式中输入参数的不确定性会引起模式模拟偏差。为了改善模式的模拟能力,减小参数的不确定性,通常要进行参数优化过程。利用温江站观测的近地层资料,结合粒子群优化算法(Particle Swarm Optimization,PSO),优化了陆面过程模式SHAW(Simultaneous Heat and Water)中难以直接观测的土壤和植被参数。在此基础上,分别利用优化后的参数和默认参数运行SHAW模式,模拟该地区陆面过程特征,并与观测值进行对比,研究优化参数后对陆面过程模拟的影响。结果表明:利用PSO算法优化SHAW模式后,能提高土壤湿度和潜热通量的模拟性能,模拟的土壤湿度和潜热通量与相应的观测值偏差减小。但与此同时,并没有改进净辐射、土壤温度和感热通量的模拟性能。说明PSO算法可以用于陆面模式参数优化,但仅仅通过参数优化并不能同时提高所有变量的模拟性能。     

混合递阶遗传径向基网络及其在副热带高压预报中的应用

采用遗传算法与径向基网络结合的方法建立了副热带高压特征指数的预报优化模型.针对径向基网络结构和初始参数难以客观确定的不足,引入混合递阶遗传算法同时优化网络结构和参数.该优化方法结合了递阶遗传算法和最小二乘法的优点,具有较高的学习效率.将混合递阶遗传径向基网络用于副高数值预报产品的预报试验和效果比较,结果表明:混合递阶遗传算法优化的径向基网络模型具有较好的收敛效果和泛化能力,对副高指数的预报效果有较明显的改进和提高.     

基于输出估计的多输入系统随机梯度估计算法

对于输出误差模型描述的多输入单输出系统,辨识的困难在于辨识模型信息向量中包含系统未知输出量(真实输出或无噪输出),以致标准辨识算法无法应用.提出了利用输出估计代替系统真实输出的辨识思想,即通过估计模型预测(估算)系统输出,利用这个估计输出来递推计算系统参数,进而提出了基于输出估计的随机梯度辨识算法,并研究了算法的收敛性,给出了仿真例子.     

基于粒子群优化的多指标组合算子的大气污染预报模型

根据多指标组合算子法建立了大气污染物浓度预报的参数模型,并采用一种新颖的粒子群优化算法对大气污染物浓度预报模型中的参数进行优化。通过实例计算,该模型同线性回归、模糊模式识别、参数化组合算子方法进行了结果比较,结果表明,所建立的模型比前三种方法平均误差率小,吻合度好,具有较好的预测效果。     

Optimal parameterization of COVID-19 epidemic models: 新冠肺炎流行病学模型的最优参数化

At the time of writing, coronavirus disease 2019 (COVID-19) is seriously threatening human lives and health throughout the world. Many epidemic models have been developed to provide references for decision-making by governments and the World Health Organization. To capture and understand the characteristics of the epidemic trend, parameter optimization algorithms are needed to obtain model parameters. In this study, the authors propose using the Levenberg–Marquardt algorithm (LMA) to identify epidemic models. This algorithm combines the advantage of the Gauss–Newton method and gradient descent method and has improved the stability of parameters. The authors selected four countries with relatively high numbers of confirmed cases to verify the advantages of the Levenberg–Marquardt algorithm over the traditional epidemiological model method. The results show that the Statistical-SIR (Statistical-Susceptible–Infected–Recovered) model using LMA can fit the actual curve of the epidemic well, while the epidemic simulation of the traditional model evolves too fast and the peak value is too high to reflect the real situation.摘要现如今, 新冠肺炎(COVID-19)严重威胁着世界各国人民的生命健康. 许多流行病学模型已经被用于为政策制定者和世界卫生组织提供决策参考. 为了更加深刻的理解疫情趋势的变化特征, 许多参数优化算法被用于反演模型参数. 本文提议使用结合了高斯-牛顿法和梯度下降法的Levenberg–Marquardt(LMA)算法来优化模型参数. 使用四个病例数相对较多的国家来验证这一算法的优势: 相较于传统流行病学模型模拟曲线过早过快的到达峰值, 应用LMA的Statistical-SIR(Statistical-Susceptible–Infected–Recovered)模型可以更好地拟合实际疫情曲线.     

基于OPTICS聚类算法的雷达数据雷暴单体识别方法

雷达数据的雷暴单体识别算法是雷暴追踪算法的重要组成部分，传统的连续区域法只能通过改变回波强度阈值来调整雷暴单体识别结果，不能满足当前应用需求。本文提出了〖JP2〗一种基于OPTICS（Ordering Points to Identify〖JP〗 the Clustering Structure）算法的雷暴识别方法，该方法能基于高回波点的密度信息进行雷暴单体识别。利用高分辨率X波段天气雷达在两次雷暴过程中的体扫数据检验了算法的效果，并与传统方法进行了比较。结果表明：该方法能克服连续区域法在高分辨率雷达数据中可能出现的无法区分不同单体、识别结果过于零散等问题。并且能在不修改回波强度阈值的情况下灵活调整输出结果，以适应不同应用需求。     

Experience of inverse modeling for estimating hydraulic parameters of unsaturated soils

Soil column experiments were carried out to inversely estimate the hydraulic parameters of the unsaturated zone. This study analyzes clay soil taken from an irrigated area of the Mnasra province in northwestern Morocco which includes large agricultural areas. Fully drained and controlled laboratory model tests and their numerical simulations are presented. The inverse modeling method was applied to estimate the hydraulic properties of unsaturated soil based on the Levenberg-Marquardt algorithm. A nonlinear estimation method tied up with the finite difference method and inversion analysis was used to minimize the cost function defined by the difference between the predicted and observed values of the model. Unsaturated hydraulic parameters of the Van Genuchten and Mualem models were estimated using water content measurements at five clay depths (10, 20, 30, 40, and 60 cm) and/or the cumulative water flux at the column bottom. The experimental hydraulic parameters and the predicted results were in good agreement with the measurements from the single and multicost function experiments. Also the results showed that the multicost function experiment was more appropriate in determining the hydraulic properties of unsaturated soils than the single-objective function experiment. The comparison between measured values and predicted results showed that the inverse analysis based on the 1D soil column experiment was efficient and useful to establish the hydraulic properties of unsaturated soils.     

PREDICTION OF FLOOD SEASON PRECIPITATION IN SOUTHWEST CHINA BASED ON IMPROVED PSO-PLS

In order to achieve the best predictive effect of the Partial Least Squares (PLS) regression model, Particle Swarm Optimization (PSO) algorithm is applied to automatically filter the optimal subset of a set of candidate factors of PLS regression model in this study. An improved version of the Particle Swarm Optimization-Partial Least Squares (PSO-PLS) regression model is applied to the station data of precipitation in Southwest China during flood season. Using the PSO-PLS regression method, the prediction of flood season precipitation in Southwest China has been studied. By introducing the precipitation period series of the mean generating function (MGF) extension as an alternative factor, the MGF improved PSO-PLS regression model was also build up to improve the prediction results. Randomly selected 10%, 20%, 30% of the modeling samples were used as a test trial; random cross validation was conducted on the MGF improved PSO-PLS regression model. The results show that the accuracy of PSO-PLS regression model and the MGF improved PSO-PLS regression model are better than that of the traditional PLS regression model. The training results of the three prediction models with regard to the regional and single station precipitation are considerable, whereas the forecast results indicate that the PSO-PLS regression method and the MGF improved PSO-PLS regression method are much better than the traditional PLS regression method. The MGF improved PSO-PLS regression model has the best forecast performance on precipitation anomaly during the flood season in the southwest of China among three models. The average precipitation (PS score) of 36 stations is 74.7. With the increase of the number of modeling samples, the PS score remained stable. This shows that the PSO algorithm is objective and stable. The MGF improved PSO-PLS regression prediction model is also showed to have good prediction stability and ability.     

北京闪电综合探测网(BLNET):网络构成与初步定位结果

北京闪电综合探测网(Beijing Lightning NETwork, 简称BLNET)由10个观测站组成, 每个子站主要由闪电快、慢电场变化测量仪(也称快、慢天线)和闪电甚高频(VHF)辐射探测仪构成, 实现了对闪电的多频段的综合观测。本文首先详细介绍了BLNET的网络构成, 然后利用蒙特卡罗法对网络的定位误差进行了理论分析, 模拟结果表明网络内部水平定位误差小于200 m, 网络外部100 km处水平定位误差小于3 km, 最后利用Chan氏算法和Levenberg-Marquardt算法相结合的方法, 对发生在2013年7月7日的一次雷暴过程分别进行了地闪和云闪定位, 将定位结果和对应时次的雷达回波进行比较, 发现地闪和云闪都出现在大于30 dBZ的雷达回波区, 表明了探测网络和定位方法的可靠性。     

Application of the Analogue-Based Correction of Errors Method in ENSO Prediction

In this study, a method of analogue-based correction of errors（ACE） was introduced to improve El Ni？o-Southern Oscillation（ENSO） prediction produced by climate models. The ACE method is based on the hypothesis that the flow-dependent model prediction errors are to some degree similar under analogous historical climate states, and so the historical errors can be used to effectively reduce such flow-dependent errors. With this method, the unknown errors in current ENSO predictions can be empirically estimated by using the known prediction errors which are diagnosed by the same model based on historical analogue states. The authors first propose the basic idea for applying the ACE method to ENSO prediction and then establish an analogue-dynamical ENSO prediction system based on an operational climate prediction model. The authors present some experimental results which clearly show the possibility of correcting the flow-dependent errors in ENSO prediction, and thus the potential of applying the ACE method to operational ENSO prediction based on climate models.     

多网格法在长期数值天气预报模式中的应

本文在简要介绍了多网格法的基本思想和本文所用的经改进的地气耦合非定常距平模式的基础上，用多网格法求解地气耦合非定常距平模式中的位势倾向方程—椭圆型方程。通过对1982～1989年6～8月的北半球环流季节预报试验表明，由于使用多网格法，迭代精度提高了，克服了原模式预报的距平场过分光滑振幅偏小的缺点，很好地解出了模式中对长期天气预报起关键作用的大尺度低频信号。在高低两种精度下进行的比较试验表明:多网格法较逐步超松弛法可以使计算速度提高1倍以上。高精度较低精度多网格法更为有效。     

基于CNN和RF算法的ECMWF降水分级订正预报方法

利用卷积神经网络(CNN)和随机森林回归模型,提出了一种新的欧洲中期天气预报中心(ECMWF)降水订正预报方法。该方法首先根据ECMWF模式对站点雨量预报值所属的等级进行划分,再计算出不同等级相对应的高相关因子矩阵。进一步利用CNN模型对高相关矩阵进行综合特征提取的学习和训练。最后对CNN模型最终输出的特征因子中,选取若干个与预报站点相关性高的特征,并与ECMWF降水量场插值到预报站点的因子一起,作为随机森林回归模型的输入因子进行预报建模。通过对10个预报试验站点未来24h降水量的分级和不分级订正预报试验,结果表明:(1)ECMWF降水量分级订正预报方法的平均绝对偏差和均方根误差分别比利用ECMWF插值到站点的预报方法减小了20%和15%;(2)24h暴雨及以上的降水分级订正预报方法的平均TS评分为0.32,也显著高于EC插值的0.19;(3)与利用同样的预报模型对全样本(不分级)的传统数值预报模式产品订正预报方法相比,本文提出的分级订正预报方法在总体预报精度和暴雨及以上的强降水预报TS评分上均有更高的预报技巧。     

Nonlinear statistical attribution and detection of anthropogenic climate change using a simulated annealing algorithm

Summary ¶The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/ underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986).In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO₂)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Niño/Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference.It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CM-FSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.     

On the Application of a Genetic Algorithm to the Predictability Problems Involving "On-Off" Switches

The lower bound of maximum predictable time can be formulated into a constrained nonlinear opti- mization problem, and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation (CNOP) method. Usually, the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method, which is named the ADJ-CNOP. However, with the increasing improvement of actual prediction models, more and more physical processes are taken into consideration in models in the form of parameterization, thus giving rise to the on-off switch problem, which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the ad- joint method. In this study, we attempted to apply a genetic algorithm (GA) to the CNOP method, named GA-CNOP, to solve the predictability problems involving on-off switches. As the precision of the filtering method depends uniquely on the division of the constraint region, its results were taken as benchmarks, and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation. Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time, even in non-smooth cases, while the ADJ-CNOP, owing to the effect of on-off switches, often yields the incorrect lower bound of maximum predictable time. Therefore, in non-smooth cases, using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients, as long as genetic operators in GAs are properly configured.