面向情报需求的区域毒品价格评估模型

面向情报需求对区域各类流通毒品价格进行建模分析,针对毒品案事件管理系统中毒品交易量及价格数据,基于聚类分析评估单类毒品零包价格,并以此构建线性加权评估模型,用该模型计算出的价格指标评估当地毒品价格水平。实验结果表明,省级的毒品价格指标相对稳定,以此构建了偏离度、平稳度综合反映所辖区域毒品打击力度,为地方禁毒部门对毒情监管提供一定的理论依据及数据支撑。

目标跟踪的非线性回归方法

讨论目标跟踪数据融合的常用非线性回归模型 ,研究参数估计的初值获取方法 ,求估计值的迭代算法 ,估计值的校准方法 ,估计值方差矩阵的估算方法 .使用本文方法处理目标跟踪数据速度快、精度高 。

基于逻辑框图的卫星健康评估方法

针对目前无法准确量化反映在轨卫星健康状态的问题,提出一种基于卫星部组件逻辑连接关系的健康状态分层评估模型,用任务健康度和任务风险度定量表示卫星状态;在部件层,选取关键遥测参数指标,通过统计历史健康数据及查阅设计资料建立部件级非线性无量纲评估模型;在组件层,通过各部件的逻辑连接关系分别建立评估模型;在分系统层,利用组件层得到的结果及各组件的逻辑连接关系,结合模糊层次分析(fuzzy analytic hierarchy process,FAHP)和变权理论,建立分系统健康状态评估模型;最后,通过某卫星正常运行时期和异常运行时期的在轨遥测数据验证,结果表明,该评估模型具有正确性。

遗传神经网络方法在课堂教学质量评估中的应用

课堂教学评估是提高教学水平,促进教学质量的有效途径.该文建立非线性评估模型,针对仅应用人工神经网络方法求解容易出现不收敛的情况,将遗传算法与BP神经网络结合应用,用遗传算法来优化网络的拓扑结构和学习参数,并筛选参与训练的评估因子,搜索出参加训练的输入参数和网络结构参数的最佳组合,从而建立具有较好预报效果的网络模式.实例计算结果表明,与未经优化的神经网络相比,具有收敛速度快,预测误差小等优点,具有较高的识别能力.

基于非线性投入产出模型的TFP测度及算法

利用非线性的投入产出模型构造了TFP测度的基本公式,再利用非线性评估模型(带有凸约束的广义最小二乘模型)解决了TFP计算中的模型参数多于观测个数的问题,利用交互投影迭代算法解决评估模型中因变量未知的问题,利用Powell算法实现非线性极值的计算,最后利用遗传算法解决Powell算法的全局收敛问题。通过这些算法的组合,完成了区域TFP的测算。

电压约束的台区分布式光伏最大接入容量研究

提出了一种以电压为约束条件的台区配电线路可消纳光伏容量快速评估方法。解决了台区配电线路的电压重过载问题。该方法通过分析光伏发电接入时线路电压的变化并建立其最大容量的评估模型,该模型的主要参数为灵敏度系数,通过求出各个线路的灵敏度系数,再利用多元线性回归法得出最大容量对各个线路参数的影响,通过对线路是否重过载的判定一定程度上实现了对台区可接入最大光伏的预测。最后对湖溪塔元公变台区、湖溪中格田台区进行实验验证了该模型的有效性。

汽轮发电机组转子振动预警方法研究

汽轮发电机组转子长期连续高速旋转,不可避免地出现转子振动状态变化,而转子振动状态的优劣直接影响整个机组安全运行,对其预警方法的研究势在必行。首先统计转子振动运行规律;其次基于振动运行规律提出转子振动异常阈值计算方法;再次依托异常阈值和非线性状态评估(Nonlinear State Estimation Technique,NSET)提出转子振动预警方法;最后利用某电厂机组运行数据进行测试验证,并与基于反向传播(Back Propagation,BP)神经网络的振动预警方法进行对比,结果表明所提方法可以准确有效实现转子振动预警。

Some Asymptotic Properties for Multivariate Partially Linear Models

The paper considers a multivariate partially linear model under independent errors,and investigates the asymptotic bias and variance-covariance for parametric component βand nonparametric component F（·）by the GJS estimator and Kernel estimation.

基于SCADA大数据的风电机组故障预警分析与研究

风电机组长期工作在恶劣的环境中,导致故障频发,运用合理高效的方法对风电机组部件进行故障预警,具有十分重要的现实意义。提出了一种基于SCADA(Supervisory Control And Data Acquisition)大数据的风电机组故障预警策略,依托现场经数据预处理的机组数据,提取出高维数据中的特征向量作为数据样本,建立非线性状态评估(NSET)模型,利用NSET模型以及历史数据确定正常工作状态下的阈值;基于SCADA数据以及预测模型的预测值参数与正常状态下的阈值进行比较,当部件工作出现异常时,预测值与实际值的残差增大,超出预先设定的阈值时发出报警信息。利用故障预警方法能及时发现潜在故障,提前排除重大事故隐患,有助于提高设备的可靠性。

Evaluating the impact of spatio-temporal scale on CPUE standardization

This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging fishery in the northwestern Pacific Ocean as an example to evaluate 24 scenarios at different spatio-temporal scales,with a combination of four levels of temporal scale(weekly,biweekly,monthly,and bimonthly)and six levels of spatial scale(longitude×latitude:0.5°×0.5°,0.5°×1°,0.5°×2°,1°×0.5°,1°×1°,and 1°×2°).We applied generalized additive models and generalized linear models to analyze the24 scenarios for CPUE standardization,and then the differences in the standardized CPUE among these scenarios were quantified.This study shows that combinations of different spatial and temporal scales could have different impacts on the standardization of CPUE.However,at a fine temporal scale(weekly)different spatial scales yielded similar results for standardized CPUE.The choice of spatio-temporal scale used in data collection and analysis may create added uncertainty in fisheries stock assessment and management.To identify a cost-effective spatio-temporal scale for data collection,we recommend a similar study be undertaken to facilitate the design of effective monitoring programs.

INTERVAL TIME SERIES ANALYSIS WITH AN APPLICATION TO THE STERLING-DOLLAR EXCHANGE RATE

Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.

Asymptotic Properties of Maximum Quasi-Likelihood Estimators in Generalized Linear Models with Diverging Number of Covariates

In this paper, for the generalized linear models （GLMs） with diverging number of covariates, the asymptotic properties of maximum quasi-likelihood estimators （MQLEs） under some regular conditions are developed. The existence, weak convergence and the rate of convergence and asymptotic normality of linear combination of MQLEs and asymptotic distribution of single linear hypothesis teststatistics are presented. The results are illustrated by Monte-Carlo simulations.

Sequential Estimate for Generalized Linear Models with Uncertain Number of Effective Variables

For the generalized linear model,the authors propose a sequential sampling procedure based on an adaptive shrinkage estimate of parameter.This method can determine a minimum sample size under which effective variables contributing to the model are identified and estimates of regression parameters achieve the required accuracy.The authors prove that the proposed sequential procedure is asymptotically optimal.Numerical simulation studies show that the proposed method can save a large number of samples compared to the traditional sequential approach.