μ ANALYSIS AND μ SYNTHESIS FOR NONLINEAR ROBUST CONTROL SYSTEMS

A μ analysis and μ synthesis method for nonlinear robust control systems was presented. The nonlinear robust contrl problem using μ method was described. By means of the nonlinear state feedback and state coordinates transformation, many uncertain nonlinear systems can be transformed as a linear fractional transformation (LFT) on the generalized plant and the uncertainty. Based on the LFT, a linear robust controller can be obtained by the DK iteration and then a corresponding nonlinear robust control law is constructed. An example was given in the paper.

Nonlinearly correlated failure analysis and autonomic prediction for distributed systems

In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.

ANALYSIS OF CRITERIA PREDICTING THE TENDENCY OF PILOT INDUCED OSCILLATION FOR LINEAR NON-LINEAR SYSTEM

Some problems encountered in applying Smith's technique to predict the PIO tendency for non-linear pilot-vehicle loop, are thoroughly analyzed. Subsequently, modified PIO predictable criteria are developed, in addition, to make also a certain improvement on Smith's PIO definition and PIO types. These modified criteria are applied to predict PIO tendency of various different configurations on the variable stability aircraft NT-33 in case of supposed non-linearity, and predicted results are compared with the flight tests and analytical results in the case of linear hypothesis given in Ref. (4)

An Analysis of the Difference between the Multiple Linear Regression Approach and the Multimodel Ensemble Mean

An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nifio3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.

The Study of Contact Pressure Analyses and Prediction of Dynamic Fatigue Life for Linear Guideways System

The application of the linear guideways is very extensive, such as automation equipment, heavy-duty carry equipment, heavy-cut machining tool, CNC grinding machine, large-scale planning machine and machining center with the demand of high rigidity and heavy load. By means of the study of contact behavior between the roller/guideway and roller/slider, roller type linear guideways can improve the machining accuracy. The goal of this paper is to construct the fatigue life model of the linear guideway, with the help of the contact mechanics of rollers. In beginning, the analyses of the rigidity of a single roller compressed between guideway and slider was conducted. Then, the normal contact pressure of linear guideways was obtained by using the superposition method, and verified by the FEM software (ANSYS workbench). Finally, the bearing life theory proposed by Lundberg and Palmgren was used to describe the contact fatigue life.

Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.

Adaptive nonlinear model predictive control design of a flexible-link manipulator with uncertain parameters

This paper presents a novel adaptive nonlinear model predictive control design for trajectory tracking of flexible-link manipulators consisting of feedback linearization, linear model predictive control, and unscented Kalman filtering. Reducing the nonlinear system to a linear system by feedback linearization simplifies the optimization problem of the model predictive controller significantly, which, however, is no longer linear in the presence of parameter uncertainties and can potentially lead to an undesired dynamical behaviour. An unscented Kalman filter is used to approximate the dynamics of the prediction model by an online parameter estimation, which leads to an adaptation of the optimization problem in each time step and thus to a better prediction and an improved input action. Finally, a detailed fuzzy-arithmetic analysis is performed in order to quantify the effect of the uncertainties on the control structure and to derive robustness assessments. The control structure is applied to a serial manipulator with two flexible links containing uncertain model parameters and acting in three-dimensional space.

Studies and experiments on earthquake prediction during 1999～2002

This paper briefly reviewed the research progresses of earthquake prediction and/or forecasting in China during 1999~2002, especially focused on mid-short term prediction methods with approaches of seismicity, crustal deformation, electromagnetism, ground water and the analysis by synthesis, and the application of the methods to the practice of earthquake prediction.

A Recursive Binary Tree Model for the Analysis of the Response to Antiretroviral Therapy of HIV Infected Adults in Burkina Faso

In this paper we aim to analyse temporal variation of CD4 cell counts for HIV-infected individuals under antiretroviral therapy by using statistical methods. This is achieved by resorting to recursive binary regression tree approach [1]?[2]. This approach has made it possible to highlight the existence of several segments of the population of interest described by the interactions between the predictive covariates of the response to the treatment regimen.

COMBINATION OF PITCH SYNCHRONOUS ANALYSIS AND FISHER CRITERION FOR SPEAKER IDENTIFICATION

A novel text independent speaker identification system is proposed. In the proposed system, the 12-order perceptual linear predictive cepstrum and their delta coefficients in the span of five frames are extracted from the segmented speech based on the method of pitch synchronous analysis. The Fisher ratios of the original coefficients then be calculated, and the coefficients whose Fisher ratios are bigger are selected to form the 13-dimensional feature vectors of speaker. The Gaussian mixture model is used to model the speakers. The experimental results show that the identification accuracy of the proposed system is obviously better than that of the systems based on other conventional coefficients like the linear predictive cepstral coefficients and the Mel-frequency cepstral coefficients.

疫情后深圳市生活垃圾产生量的预测及变化分析

疫情后针对生活垃圾产生量的变化,开展预测并对结果进行分析,是地方政府科学制定相关规划、合理布局环卫设施建设的重要依据。因此,立足深圳市疫情前后数据,结合灰色关联度模型、多元线性回归等方法,分析2022—2035年生活垃圾产生量预测值的变化。结果表明,生活垃圾产生量保持逐年增长趋势,规划期末水平年(2035年)的具体数值有一定程度调整。预测到2035年的生活垃圾产生量为45 547 t/d,其中再生资源回收量为14 576 t/d,生活垃圾处理处置量为30 971 t/d。建议各项环卫设施建设可灵活调整设施规模及建设周期,提升城市垃圾处理效能。

基于GRA-GRU的淮河流域水质预测研究

水质指标具有多元相关性、时序性和非线性的特点,为有效预测河流水质变化,针对水质数据存在缺失和异常的问题,提出基于灰色关联分析-门控循环单元(Grey Relational Analysis-Gated Recurrent Unit, GRA-GRU)的水质预测模型。以淮河流域水质数据为样本,使用线性插值修补缺失数据和剔除的异常数据。使用灰色关联分析计算不同水质指标间的相关性,选择高相关性的水质指标以确定输入变量,并使用门控循环单元(Gated Recurrent Unit, GRU)预测不同的水质指标。将GRA-GRU的预测结果与反向传播神经网络(Back Propagation Neural Network, BPNN)、循环神经网络(Recurrent Neural Network, RNN)、长短期记忆神经网络(Long Short Term Memory, LSTM)、GRU及灰色关联分析-长短期记忆神经网络(Grey Relational Analysis-Long Short Term Memory, GRA-LSTM)进行对比分析,结果显示GRA-GRU在不同水质指标预测上具有较好的适应性,可以有效降低预测误差。其中,与其他模型相比,GRA-GRU预测的化学需氧量在均方根误差上分别降低了3.617%、0.681%、0.478%、1.505%和0.471%。

裂缝性低渗油藏压裂水平井井网渗流数学模型

基于分形理论表征天然裂缝和压裂裂缝网络的复杂裂缝形态,针对裂缝性低渗透储层的非线性渗流特征和储层压敏特性,建立了裂缝性低渗透油藏压裂水平井-直井井组的非线性渗流模型。根据流体在混合井网中的不同流动形态,划分为3个区域,推导出了混合井网三区耦合产能公式,分析了分形维数、压敏效应、裂缝长度等参数对井网产能的影响。

基于慢特征分析与最小二乘支持向量回归集成的草酸钴合成过程粒度预报

草酸钴合成过程是钴湿法冶炼的关键单元操作,其粒度分布是重要的质量指标,然而难以在线实时测量。同时,草酸钴合成过程通常存在非线性、多约束和慢时变特征。因此,提出一种集成慢特征分析(slow feature analysis,SFA)与最小二乘支持向量回归(least square support vector regression,LSSVR)的草酸钴粒度预报模型对草酸钴合成过程质量指标实现在线测量。在该方法中,首先,SFA方法可以有效地捕获过程的慢特征向量,解决慢时变问题;然后,利用LSSVR方法建立慢特征与粒度之间的非线性关系模型,进而实现质量指标在线预报。最后,应用非线性的数值案例以及草酸钴合成过程数据,验证该方法的有效性。实验结果显示:相较于单一的径向基函数神经网络(radial basis function neural network,RBFNN)、LSSVR预测模型以及SFA与NN相结合的预报模型,所提方法在数值案例中的预测精度分别提升了13.31%、2.26%、1.72%;在草酸钴合成过程中的预测精度分别提升了13.27%、9.96%、8.92%。

基于多元线性回归的数学建模成绩预测研究

利用机器学习中的多元线性回归方法建立了数学建模成绩的预测模型。首先,选择课程并进行数据清洗,确定数据集。其次,利用相关性分析和方差膨胀因子检验课程成绩间的相关性和多重共线性,通过逐步回归的方式确定数学建模成绩的影响因素和回归模型。最后,利用交叉验证,在不同的训练集上训练模型,利用均方误差和平均相对误差检验模型的预测准确性。结果表明,采用多元线性回归预测的数学建模成绩与实际成绩相近,预测模型有效。

基于历史统计数据分析的全国原煤产量预测研究

煤炭是我国经济建设中的重要能源,其市场供需稳定有着不可忽视的重要性,准确预测我国煤炭产量有助于国家能源政策的制定。故选取2003—2023年国有原煤产量历史数据以构建预测数据库,并介绍了预测煤炭产量的6种传统预测方法。分别采用6种方法进行对应2024年的国有原煤产量预测,对比分析各方法的预测准确程度。综合各研究结果给出了2024年国有原煤产量预测区间,同时分析得出加权移动平均法和一元线性回归预测法具有更好的拟合效果。

A NEW METHOD FOR PREDICTING TROPOSPHERIC RANGE ERROR

Based on the quasi-measured values of tropospheric refraction,the relation betweenand △R as expressed in Eq.(3)is proved,and according to the stable feature of refractivityat 9 km above sea level,a simplified method for predicting tropospheric range error is analysed.Some new parameters for linear regression analysis of tropospheric range error are given also.

Kautz Function Based Continuous-Time Model Predictive Controller for Load Frequency Control in a Multi-Area Power System

A continuous-time Model Predictive Controller was proposed using Kautz function in order to improve the performance of Load Frequency Control(LFC).A dynamic model of an interconnected power system was used for Model Predictive Controller(MPC)design.MPC predicts the future trajectory of the dynamic model by calculating the optimal closed loop feedback gain matrix.In this paper,the optimal closed loop feedback gain matrix was calculated using Kautz function.Being an Orthonormal Basis Function(OBF),Kautz function has an advantage of solving complex pole-based nonlinear system.Genetic Algorithm(GA)was applied to optimally tune the Kautz function-based MPC.A constraint based on phase plane analysis was implemented with the cost function in order to improve the robustness of the Kautz function-based MPC.The proposed method was simulated with three area interconnected power system and the efficiency of the proposed method was measured and exhibited by comparing with conventional Proportional and Integral(PI)controller and Linear Quadratic Regulation(LQR).

基于PCA-RVM的矿山岩石爆破粒径预测模型

为解决露天矿山爆破开采过程中岩石爆破粒径大小难以获取的问题,提出一种基于主成分分析法(PCA)及相关向量机(RVM)相结合的矿山岩石爆破粒径预测模型.该模型利用PCA对样本数据进行降维处理,选取出4个相互独立的主成分变量,并借助RVM构建主成分与爆破粒径之间的非线性映射关系,从而建立预测模型.将该模型应用于工程实例,并与BP神经网络和LM双隐含层模型进行对比.结果表明,在相同学习样本下,PCA-RVM模型预测结果与实际值更加接近,在平均相对误差和均方差上远小于另两种模型.

基于多元线性逐步回归和BP神经网络建立鸭梨盛花期预测模型

本研究基于2002—2020年河北省魏县鸭梨盛花期观测资料和气象数据,采用线性趋势法揭示其盛花期变化趋势,并通过相关性分析筛选出显著影响盛花期的气象因子,然后分别用BP神经网络、多元线性逐步回归方法建立盛花期预测模型,以决定系数、均方根误差、预测准确/误差率为评判指标对模型预测精度进行评价。结果表明,2002—2020年魏县鸭梨盛花期呈现提前趋势,每10年平均提前2.4天。有13个气象因子与盛花期极显著相关(P<0.010),相关系数在-0.575~-0.852。两种预测模型均可在3月上旬对盛花期进行预报,基于最早盛花期可提前13天预报,基于最晚盛花期可提前29天预报。多元线性逐步回归模型的R^(2)为0.905,RMSE为1.45,R_(d1)为94.7%,R_(d2)为5.3%;BP神经网络的R^(2)为0.950,RMSE为1.05,R_(d1)为100%,R_(d2)为0;用2021和2022年的数据对两个模型的预测效果进行验证,除多元线性逐步回归模型预测的2021年盛花期日序数与实际日序数差2天外,两模型对两年盛花期的预测值与实测值一致。综合来看,BP神经网络模型的预测效果更好,准确率更高,可用于鸭梨盛花期预测,这为制定河北魏县鸭梨花期管理措施及梨花节活动方案奠定了基础。