A New Approach for Parameter Optimization in Land Surface Model

In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM （common land model,CoLM）.Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project （CEOP） that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments：First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux （SH）,latent heat flux （LH）,soil temperature （TS）,and moisture （MS） at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.

Genetic programming-based chaotic time series modeling

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

Parameter estimation of cutting tool temperature nonlinear model using PSO algorithm

In cutting tool temperature experiment, a large number of related data could be available. In order to define the relationship among the experiment data, the nonlinear regressive curve of cutting tool temperature must be constructed based on the data. This paper proposes the Particle Swarm Optimization (PSO) algorithm for estimating the parameters such a curve. The PSO algorithm is an evolutional method based on a very simple concept. Comparison of PSO results with those of GA and LS methods showed that the PSO algorithm is more effective for estimating the parameters of the above curve.

在线优化参数的多变量无模型预测线性自抗扰控制

针对时变参数的多变量线性模型上叠加非线性的系统,采用具有辅助向量的多变量偏格式动态线性化方法逼近该系统,在多变量线性状态观测器中加入辨识的线性模型,提高状态观测精度。在多变量线性跟踪微分器和状态观测器分别引入超前参考输入与输出预测值,使多变量线性自抗扰控制器具有预测控制性能。运用非线性递推最小二乘法在线优化多变量线性自抗扰控制器的参数,给出在线优化参数的多变量无模型预测线性自抗扰控制算法。结果表明:无参数优化时的响应曲线,超调大,调节时间约1 s,无稳偏;有参数优化时的响应曲线,超调较小,调节时间约0.65 s,无稳偏,控制算法的响应优良。

哈里斯鹰算法在广义非线性马斯京根参数优化中的应用——以洛河为例

马斯京根模型在河道洪水演算中发挥着重要作用,其演算精度在于参数的优选。针对目前马斯京根参数率定中存在的求解复杂、精度不高等问题,提出利用哈里斯鹰算法对其参数进行优化,这种方法具有广泛的全局搜索能力,且需要调节的参数较少。以黄河支流洛河为研究对象,利用广义非线性马斯京根模型对宜阳—白马寺段的河道进行洪水演算,且分别用哈里斯鹰算法、粒子群算法和蚁群算法对其参数进行优化。结果表明,基于哈里斯鹰算法的广义非线性马斯京根模型在洛河宜阳—白马寺段的演算精度较高,其Min.SSD为1237,洪峰误差DPO仅为5,均优于粒子群算法和蚁群算法优化后的结果,其成果适合应用于洛河宜阳—白马寺段的洪水预报工作。

Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander

A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander （KLM） state using the conditional nonlinear optimal parameter perturbation （CNOP-P） method. Because of their relatively large uncertainties, three model parameters were considered： the interracial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 （SPG2） algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation （CNOP-I） method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.

Application of Conjugate Gradient Approach for Nonlinear Optimal Control Problem with Model-Reality Differences

In this paper, an efficient computational algorithm is proposed to solve the nonlinear optimal control problem. In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. The aim of applying this model is to take into account the differences between the real plant and the model used during the calculation procedure. In doing so, an expanded optimal control problem is introduced such that system optimization and parameter estimation are mutually interactive. Accordingly, the optimality conditions are derived after the Hamiltonian function is defined. Specifically, the modified model-based optimal control problem is resulted. Here, the conjugate gradient approach is used to solve the modified model-based optimal control problem, where the optimal solution of the model used is calculated repeatedly, in turn, to update the adjusted parameters on each iteration step. When the convergence is achieved, the iterative solution approaches to the correct solution of the original optimal control problem, in spite of model-reality differences. For illustration, an economic growth problem is solved by using the algorithm proposed. The results obtained demonstrate the efficiency of the algorithm proposed. In conclusion, the applicability of the algorithm proposed is highly recommended.

广州“5.7”暴雨预报的模式不确定性研究

2017年5月7日,在弱天气尺度强迫下,广州发生了暖区特大暴雨,局地发展迅速,降水强度极端,多家业务模式出现了漏报情况。为了探究此次降水过程模式预报的不确定性,采用条件非线性最优参数扰动(Conditional Nonlinear Optimal Perturbation related to Parameters,CNOP-P)方法筛选出最能体现中小尺度系统非线性误差增长特征的关键物理参数,以此构造CNOP-P-RP模式扰动方案,并基于CMA-Meso模式进行对流尺度集合预报试验,最后探究了CNOP-P关键参数影响局地对流发生、发展不同阶段的物理机理。结果显示,不同降水阶段的CNOP-P敏感参数主要与垂直扩散、云雨自动转换或其他水成物向雨滴的转换有关。与业务上常用的随机物理倾向扰动(Stochastically Perturbed Parameterization Tendencies,SPPT)方案相比,在本次降水过程中,基于CNOP-P-RP方案构造的集合预报试验具有更高的降水和地面要素的概率预报技巧,集合预报系统可靠性也占优。进一步分析发现,垂直扩散不确定性导致的山前温度梯度和地面冷池的变化在对流触发和暴雨发展中起重要作用。7日00—04时(北京时,下同),花都强降水中心附近垂直扩散的增强使热量、动量和水汽的垂直输送加强,由此造成的雪、霰粒子融化增多是降水量增大的主要原因,说明该阶段雨滴的形成虽以云水的凝结碰并为主,但冰相粒子的作用不容忽视;7日04—08时,随着水汽输送和上升运动增强,更活跃的暖雨过程主导了增城强降水中心降水量的增大。该研究初步证明CNOP-P-RP方案在刻画对流尺度模式不确定性方面的可行性,可为华南暖区暴雨预报的改进提供一定参考。

灰色组合预测模型优化及科技人才需求预测

科技人才需求预测是国家合理制订人才政策的重要依据。为此,本文基于科技人才需求的数据特征,构建适用于科技人才需求预测的新型离散灰色模型FODGM(r,1,kθ,u),该模型实现了系统发展灰信息非线性规律的较好反映以及累加阶数作用范围全实域拓展,缓解了原始序列中极值对模型性能的影响,能够有效模拟科技人才需求的发展趋势与演变规律。应用该模型对我国科技人才需求量进行预测,结果显示未来我国科技人才需求量呈逐步上升趋势,预计2026年我国科技人员全时当量将达729.258万人年,科技人才需求端压力较大。相关部门可以根据预测结果制定缓解我国科技人才需求端压力的对策。

一种改进的无模型自适应控制优化方法

针对一类复杂非线性系统提出了一种改进的无模型自适应控制(improved model⁃free adaptive control,IM⁃FAC)方法,该方法通过对传统无模型自适应控制(model⁃free adaptive control,MFAC)方法控制器输出准则函数引入系统输出变化量来改善系统的动态性能。分析了IMFAC方法的稳定性和收敛性,并针对IMFAC方法中5个参数整定困难的问题,提出了基于灰狼优化(grey wolf optimization,GWO)算法的控制器参数优化方法(G-IMFAC)。对于GWO适应度函数中3个优化指标权重的最优分配问题,通过计算各个优化指标的信息熵来确定其权重。最后通过仿真模拟对比了G-IMFAC方法与传统MFAC方法及IMFAC方法的性能,结果表明G-IMFAC方法具有更小的超调量和更短的调节时间,进一步提高了系统的控制性能。

全向移动机械臂的混合视觉伺服与参数优化

面向全向移动机械臂的视觉控制任务,研究了一种基于混合视觉伺服的控制律设计方法,并采用蝙蝠算法(Bat Algorithm,BA)实现了运动轨迹优化。首先,为实现全向底盘和六自由度机械臂的同时控制,建立了全向移动机械臂整体的速度级运动学模型;进而,根据眼在手中的相机配置方案,分别推导了位置误差和图像误差的雅克比矩阵,在联合了机器人运动学模型的基础上,设计了全向移动机械臂的混合视觉伺服控制律;进一步设计了基于蝙蝠算法的控制器参数优化方法,使机械臂末端能够沿着尽可能短的路线到达期望位置;最终,通过稳定性证明和系统仿真实验说明了该算法的有效性。

在线优化参数的多变量非线性预测函数控制

针对实用随机多变量NARMAX模型的控制问题,利用多变量非线性递推最小二乘法进行参数估计,使用在动态工作点处的动态切平面逼近该模型,转化为时变多变量CARMAX模型,由其构成递推预测模型进行预测,基于多变量CARMAX模型的预测函数控制,运用非线性递推最小二乘法优化控制输入输出的加权系数,给出在线优化加权系数的多变量非线性预测函数控制算法。结果表明,系统的控制响应具有优良的性能,无超调和稳偏,调节时间约5个采样周期。

改进人工生态系统优化算法解决光伏模型参数辨识问题

光伏模型同时具有非线性和多模态的特点,传统算法在对其参数识别时易陷入局部最优,且识别精度不足。提出了一种改进的人工生态系统优化算法(IAEO),通过引入非线性控制参数调整策略来平衡探索与开发的关系,利用混沌的遍历性和非重复性来增强算法的探索能力。仿真实验表明,在单、双和三二极管和光伏组件模型上,改进算法的参数识别精度均超过99.9%,相较于原算法的RMSE值在四种模型上平均提高5.5%,和五种先进算法对比具有较强的优势。采用厂商真实数据对薄膜、单晶和多晶3种光伏组件在不同的光照和温度条件下进行测试,改进算法在不同环境中依然保持较高的准确性和稳定性。

基于控制场的高精度相机标定技术研究

相机标定是高精度视觉测量的基础,是视觉测量的关键技术。相机参数的标定精度直接影响最终的测量精度。针对大视场的高精度测量,文章提出了一种基于控制场的相机标定方法。首先,通过构建相机参数求解模型,利用DLT(直接线性变换法)获得相机参数初值,再利用L-M最优化方法求解最优化模型参数即要标定的相机参数。本标定方法的标定结果平均反投影误差达到0.1个像素点,表明本标定算法具有很高的标定精度,可以满足视觉测量的高精度测量需求,在工业测量中具有较高的使用价值。

复合粒子群优化算法在模型参数估计中的应用

化工非线性模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值。粒子群算法操作简便、容易实现且全局搜索功能较强,适用于非线性参数估计。但其参数值的确定与问题相关,若设定不当,会严重影响全局搜索的性能。今提出引入遗传算法,在粒子群算法的搜索过程中,逐代优选参数,包括惯性权值,加速常数,以此构建为复合粒子群优化算法。分析与测试表明,其全局搜索性能有显著改善。进一步的工作又将两种粒子群算法成功地应用于重油热解模型的参数估计。采用复合粒子群优化算法估计参数构建的重油热解模型,其预报相对误差比常规粒子群优化算法降低了8.97%,比简单遗传算法降低了23.21%,效果明显。

基于改进PSO算法的发酵过程模型参数估计

建立准确的非线性机理模型是发酵过程优化调控的关键。提出了一种基于改进粒子群优化算法(PSO,particle swarm optimization)的发酵过程模型参数估计方法,并将该方法用于青霉素发酵过程建模。改进的PSO算法通过引入粒子群能量对粒子进行自适应分群以防止粒子陷入局部最优,从而保证收敛于全局最优解。实验结果表明,该方法可以有效地实现青霉素发酵过程模型参数的准确估计,所得到的模型精度能够满足发酵过程的状态估计和控制需求。

基于粒子群优化的非线性灰色Bernoulli模型在中长期负荷预测中的应用

将非线性灰色Bernoulli模型用于到中长期电力负荷预测,提出了优选模型参数的粒子群优化算法。该模型是将GM(1,1)模型与Bernoulli微分方程相结合的一种灰色模型,适用于对不同发展趋势曲线的预测。通过粒子群优化算法,以模型预测平均绝对百分误差最小为目标,选择最优的模型参数。采用不同测试数据以及实际电网负荷数据进行了验证,结果表明上述模型有很好的适应性及较高的预测精度。

辨识Hammerstein模型方法研究

提出了一种对单输入单输出Hammerstein模型的参数辨识方法。基本思想是:首先,将Hammerstein模型转换为一类中间模型。然后,提出利用一种改进的粒子群优化(improved particle swarm optimization,IPSO)算法获得中间模型的参数估计值。接着,通过相应的数学关系来达到对Hammerstein模型的辨识。最后,在数值仿真中,与使用其它辨识方法进行了比较,其结果表明了所给的参数辨识方法是切实可行的。

电液半主动馈能悬架非线性建模与协调性优化

为了协调电液半主动馈能悬架的减振性能与馈能性能,建立了基于电液作动器的半主动馈能悬架的非线性模型,开展了力学特性试验,并采用最小二乘递推算法对系统模型进行了参数辨识。分析了电液作动器主要参数对悬架减振性能和馈能性能的影响,采用遗传算法对电液作动器主要参数进行了优化,并进行了台架试验。试验结果表明,频率2Hz、幅值30mm的正弦路谱输入下,优化后的电液半主动悬架簧载质量加速度降低22.23%,馈能功率提高40.51%,在保证满足一定的车辆乘坐舒适性和行驶安全性的要求下,悬架馈能性能得到明显提高。