基于改进的Logistic阻滞增长模型的人口预测研究

人口对中国经济发展和能源需求影响重大,为了预测2030年中国的人口数量,文章基于改进的Logistic阻滞增长模型,采用了3种参数估计方法进行求解,包括向后差分线性最小二乘参数估计、向前差分线性最小二乘参数估计、非线性最小二乘参数估计。相比于其他方法,基于非线性最小二乘参数估计法采用改进后的Logistic阻滞增长模型求解的拟合优度与平均相对误差更优,更适合进行中国人口数量的预测。其中1960—1969年预测的中国人口数量相较于实际的人口数量存在一定异常,通过3次样条插值法对该时间段的人口数据进行修正能使预测效果更好。计算结果表明:中国2030年人口预计将达到145 353万人。根据中国人口预测数据,建议政府在未来的经济和能源规划中,加快推进能源结构转型,促进经济可持续发展。

基于高压输电线路双端故障测距的优化算法及评估

基于分布参数模型的不同步双端测距算法,结合非线性状态估计法和采样数据比值法提出了对应的优化方案,并对两种算法的可靠性和准确性进行了评估.通过ATP仿真和Matlab计算验证了算法的有效性.

顾及多因子的香港地区加权平均温度Bevis精化模型

针对Bevis模型在香港地区精度较低的问题,基于香港地区探空站连续20 a(2000—2019年)的实测数据,建立顾及多因子的香港地区加权平均温度(T_(m))Bevis精化模型。首先,分析T_(m)与地面温度T_(s)、水汽压e_(s)、气压P之间的线性相关性;其次,采用快速傅里叶变换探测到T_(m)具有明显的年周期变化;最后在Bevis模型的基础上,综合考虑T_(s)、e_(s)、P以及年周期变化,基于非线性最小二乘法建立香港地区加权平均温度Bevis精化模型BTm。以2020年香港地区探空数据数值积分法计算的T_(m)作为参考值,对BTm模型进行精度检验,并与Bevis模型和GPT2w模型进行比较分析。结果表明:BTm模型具有相对较好的精度,其年均偏差和均方根误差(RMS)分别为-0.11 K和1.64 K,相比Bevis全球模型、GPT2w-1模型和GPT2w-5模型,其精度分别提高46.4%、35.7%和37.2%。

铸铝ZL101与树脂砂型之间等效换热系数的数值模拟

采用ZL0 1铝合金作为成型材料 ,用呋喃树脂砂作为造型材料 .采用 2 4通道的数据采集系统采集了铸件凝固与冷却过程中铸型、铸件内和铸件表面温度与时间的变化规律 .从而为等效换热系数的反问题数值计算提供了第一手的数据 .对两平板铸件进行了换热反问题的计算 ,其结果得出了两板件上下表面的等效换热系数KE 与时间的变化关系 .可以看出等效换热系数在凝固刚开始时达到一个峰值 ,而后逐渐减小 ,并且其大小与铸件的横截面积有关 ,这些为今后数值模拟中确定等效热交换系数KE

证券投资频度风险的计量与控制研究

证券投资的频度风险描述的是在规定的时间内不利结果出现的频繁程度,反映的是风险负面性的紧迫程度.尽管人们在风险度量中认识到了负面性的紧迫程度,但对频度风险的测度和控制,往往局限于定性分析,且缺乏理论支持.从证券投资频度风险的本质属性分析入手,从定量的角度出发,通过引入频度系数,提出了频度风险的测度模型,并给出了频度风险的估计方法,特别是频度系数的两种估计方法;最后,从理论上证明通过构建证券组合,可有效降低频度风险,为频度风险的深入研究提供有益参考.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Estimating the Predictability of the Quasi-Biweekly Oscillation Using the Nonlinear Local Lyapunov Exponent Approach

The quasi-biweekly oscillation (QBWO) is a major intraseasonal variability (ISV) in the tropics. Based on bandpass-filtered outgoing longwave radiation (OLR) and wind field data, the predictability limits of the QBWO in boreal summer and boreal winter are investigated using the nonlinear local Lyapunov exponent (NLLE) approach The analysis shows that the evolution of the mean error growth of the QBWO in boreal summer and the evolution of the mean error growth in boreal winter are comparable Both curves exhibit rapid growth in the initial stage followed by a slowly fluctuating, ascending trend before saturation is reached. As a result, the potential predictability limits for the boreal summer QBWO are very close to those for the boreal winter QBWO, with a lead time of approximately three weeks. Given the current limitations in the simulation and prediction of ISV, including the QBWO, the results of this study provide a useful reference for assessing the predictability of the QBWO using model simulations.

Free Search Algorithm Based Estimation in WSN Location

This paper proposes a novel intelligent estimation algorithm in Wireless Sensor Network nodes location based on Free Search,which converts parameter estimation to on-line optimization of nonlinear function and estimates the coordinates of senor nodes using the Free Search optimization.Compared to the least-squares estimation algorithms,the localization accuracy has been increased significantly,which has been verified by the simulation results.

Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-erful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in ef-ficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the pres-ence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be es-timated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of de-cision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a con-tinuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems

In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

All-Digital Self-Interference Cancellation in Zero-IF Full-Duplex Transceivers

In this paper,a general scheme in digital self-interference cancellation at baseband for zero-IF full-duplex transceivers is presented. We model the self-interference signals specifically with only the nonlinear distortion signals falling in receiving band considered. A joint estimation algorithm is proposed for compensating the time delay and frequency offset taking into account the IQ amplitude and phase imbalances from mixers. The memory effect and nonlinear distortion are adaptively estimated by the de-correlated normalized least mean square(DNLMS) algorithm. Numerical simulation results demonstrate that the proposed self-interference cancellation scheme can efficiently compensate the self-interference and outperform the existing traditional solutions.

New Algorithm Model for Processing GeneralizedDynamic Nonlinear Data Derived from Deformation Monitoring Network

The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years. As a result, many linear methods and nonlinear methods have been developed. But the methods for processing generalized nonlinear surveying and mapping data, especially for different data types and including unknown parameters with random or nonrandom, are seldom noticed. A new algorithm model is presented in this paper for processing nonlinear dynamic multiple-period and multiple-accuracy data derived from deformation monitoring network.

A novel robust approach for SLAM of mobile robot

The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the ＂particle depletion＂ phenomenon. A kind of PSO ＆ Hjj-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H~ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO （particle swarm optimization） is presented to solve the ＂particle depletion＂ phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.

利用酶动力学拟合在密闭条件下香菇呼吸速率方程及米氏方程

对香菇分别在273K、283K和293K的密闭容器中氧气和二氧化碳随时间、浓度的变化进行了测定,根据酶动力学原理,利用非线性估计法、多重回归分析分别获得气体成分的变化率曲线和米式方程,从而获得相应的参数,求得反映呼吸状态的呼吸熵动态变化规律以及温度影响参数——活化能,并以此求出在任意温度、有氧呼吸气体环境条件下果蔬的最大呼吸速率,为气调包装系统设计提供理论依据。