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.

NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS

In the industrial process situation, principal component analysis (PCA) is ageneral method in data reconciliation. However, PCA sometime is unfeasible to nonlinear featureanalysis and limited in application to nonlinear industrial process. Kernel PCA (KPCA) is extensionof PCA and can be used for nonlinear feature analysis. A nonlinear data reconciliation method basedon KPCA is proposed. The basic idea of this method is that firstly original data are mapped to highdimensional feature space by nonlinear function, and PCA is implemented in the feature space. Thennonlinear feature analysis is implemented and data are reconstructed by using the kernel. The datareconciliation method based on KPCA is applied to ternary distillation column. Simulation resultsshow that this method can filter the noise in measurements of nonlinear process and reconciliateddata can represent the true information of nonlinear process.

Nonlinear Dimensionality Reduction and Data Visualization:A Review

Dimensionality reduction and data visualization are useful and important processes in pattern recognition. Many techniques have been developed in the recent years. The self-organizing map （SOM） can be an efficient method for this purpose. This paper reviews recent advances in this area and related approaches such as multidimensional scaling （MDS）, nonlinear PC A, principal manifolds, as well as the connections of the SOM and its recent variant, the visualization induced SOM （ViSOM）, with these approaches. The SOM is shown to produce a quantized, qualitative scaling and while the ViSOM a quantitative or metric scaling and approximates principal curve/surface. The SOM can also be regarded as a generalized MDS to relate two metric spaces by forming a topological mapping between them. The relationships among various recently proposed techniques such as ViSOM, Isomap, LLE, and eigenmap are discussed and compared.

A Novel Robust Nonlinear Dynamic Data Reconciliation

Outlier in one variable will smear the estimation of other measurements in data reconciliation （DR）. In this article, a novel robust method is proposed for nonlinear dynamic data reconciliation, to reduce the influence of outliers on the result of DR. This method introduces a penalty function matrix in a conventional least-square objective function, to assign small weights for outliers and large weights for normal measurements. To avoid the loss of data information, element-wise Mahalanobis distance is proposed, as an improvement on vector-wise distance, to construct a penalty function matrix. The correlation of measurement error is also considered in this article. The method introduces the robust statistical theory into conventional least square estimator by constructing the penalty weight matrix and gets not only good robustness but also simple calculation. Simulation of a continuous stirred tank reactor, verifies the effectiveness of the proposed algorithm.

CONSERVATIVE ESTIMATING FUNCTION IN THE NONLINEAR REGRESSION MODEL WITH AGGREGATED DATA

The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.

THE MIXED PROBLEM FOR A CLASS OF NONLINEAR SYMMETRIC HYPERBOLIC SYSTEMS WITH DISCONTINUOUS DATA

This paper studies the nonlinear mixed problem for a class of symmetric hyperbolic systems with the boundary condition satisfying the dissipative condition about discontinuous data in higher dimension spaces, establishes the local existence theorem by using the method of a prior estimates, and obtains the structure of singularities of the solutions of such problems.

Sampled-data Observer Design for a Class of Stochastic Nonlinear Systems Based on the Approximate Discrete-time Models

In this paper,we studied the approximate sampleddata observer design for a class of stochastic nonlinear systems.Euler-Maruyama approximation was investigated in this paper because it is the basis of other higher precision numerical methods,and it preserves important structures of the nonlinear systems.Also,the form of Euler-Maruyama model is simple and easy to be calculated.The results provide a reference for sampled-data observer design method for such stochastic nonlinear systems,and may be useful to many practical control applications,such as tracking control in mechanical systems.And the effectiveness of the approach is demonstrated by a simulation example.

TESTING FOR VARYING DISPERSION OF LONGITUDINAL BINOMIAL DATA IN NONLINEAR LOGISTIC MODELS WITH RANDOM EFFECTS

In this paper, it is discussed that two tests for varying dispersion of binomial data in the framework of nonlinear logistic models with random effects, which are widely used in analyzing longitudinal binomial data. One is the individual test and power calculation for varying dispersion through testing the randomness of cluster effects, which is extensions of Dean(1992) and Commenges et al (1994). The second test is the composite test for varying dispersion through simultaneously testing the randomness of cluster effects and the equality of random-effect means. The score test statistics are constructed and expressed in simple, easy to use, matrix formulas. The authors illustrate their test methods using the insecticide data (Giltinan, Capizzi & Malani (1988)).

Stabilization for nonlinear systems via a limited capacity communication channel with data packet dropout

This paper addresses the stabilization problem for a class of nonlinear systems. It is assumed that the controller can only receive the transmitted sequence of finite coded signals via a limited digital communication channel. Both state and output feedback coder-decoder-controller procedures are proposed. Stabilization conditions involving the size of coding alphabet, the sampling period, system state growth rate and data packet dropout rate are obtained. Finally, an example is given to illustrate the design procedures and effectiveness of the proposed results.

非仿射非线性系统控制综述

作为仿射非线性系统更一般化的描述,非仿射非线性系统所对应的实际应用更加广泛,也更贴近实际。因此,研究非仿射非线性系统的控制问题十分重要。然而,非线性系统的非仿射特性会使控制信号以非线性函数形式出现在闭环系统中,从而带来诸如控制方向未知、奇异、过零等问题。由此,解决非仿射非线性系统的控制问题面临着巨大的挑战。基于此,介绍了仿射、严格反馈和高阶系统的相关背景知识,总结和分析了解决非仿射非线性系统控制的3种解决思路,包括函数变换法、参考模型法和数据驱动法。在已有的研究成果基础上,指出非仿射非线性系统研究领域所面临的挑战和发展趋势。

Calibration of Four Nonlinear Failure Envelopes from Triaxial Test Data and Influence of Nonlinearity on Geotechnical Computations

It is now recognized that many geomaterials have nonlinear failure envelopes. This non-linearity is most marked at lower stress levels, the failure envelope being of quasi-parabolic shape. It is not easy to calibrate these nonlinear failure envelopes from triaxial test data. Currently only the power-type failure envelope has been studied with an established formal procedure for its determination from triaxial test data. In this paper, a simplified procedure is evolved for the development of four different types of nonlinear envelopes. These are of invaluable assistance in the evaluation of true factors of safety in problems of slope stability and correct computation of lateral earth pressure and bearing capacity. The use of the Mohr-Coulomb failure envelopes leads to an overestimation of the factors of safety and other geotechnical quantities.

非线性均衡约束下的光纤数据抗干扰通信方法

为了更加精准地解决非线性扰动变量对光纤通信造成的影响,消除扰动噪声对通信速率及通信质量的负面影响,结合扰动参量的非线性特征,对其约束均衡参量进行逐级优化,通过对其常规状态下光纤数据通信扰动模型的建立与抗扰条件分析、扰动信号的非线性分布范围确定与非线性均衡约束下扰动信号优化,实现扰动参量的彻底消除,同时提升原有光纤数据传输能力.通过创建的仿真数据对比证明提出方法的抗扰效果真实有效,其优化效果能够满足现阶段全场景应用需要,切实解决光纤数据通信扰动问题.

含有输入时滞的非线性系统的输出反馈采样控制

针对含有输入时滞和低阶非线性项的非线性系统,提出一种基于采样机制的无记忆输出反馈控制方法.该方法移除了传统预测控制方法预测映射难以确定的限制,同时避免了时滞依赖方法对过去时刻状态信息的依赖性,在实际中更易实现.首先,根据系统输出在采样时刻的信息,利用加幂积分技术和齐次占优思想设计了无记忆输出反馈采样控制器.然后,利用齐次系统理论提出了闭环系统的稳定性条件.最后,仿真结果验证了所提方法的有效性和优越性.

基于深度学习的农产品期货价格预测研究

提高农产品期货价格的预测能力可为投资者的投资交易和政府宏观调控提供一定借鉴.在对LSTM、GRU、BiLSTM三种深度学习模型进行对比研究的基础上,通过添加随机种子稳定预测结果、使用一阶差分降低价格预测滞后性、用正则化、回调函数等方法解决过拟合问题,对LSTM模型进行优化.利用大连商品交易所农产品期货数据,将优化后的模型应用于玉米、黄大豆1号、鸡蛋三种农产品期货的价格预测.预测结果评价指标表明,优化LSTM模型的均方根误差为17.04,平均绝对误差为13.94,误差分别降低了38.6%和33.6%.优化的深度学习模型能够用于预测农产品期货价格,为投资交易提供借鉴.

基于数据驱动的牵引逆变器电压传感器间歇性故障诊断方法

由于间歇性故障具有随机性与不可预测性,传统的故障诊断方法难以适用于间歇性故障的诊断。为了提高牵引逆变系统的可靠性,文中提出一种针对直流侧电压传感器间歇性故障的智能诊断方法。首先,结合非线性自回归动态网络结构与极限学习机快速挖掘历史数据与异步电机定子电流之间的非线性映射关系,得到牵引电机定子电流预测器;随后,设计滑动时间窗口构建电流残差,检测间歇性故障的出现与消失时间,以此获取表征间歇性故障严重程度的评价指标。所提方法基于快速控制原型实验平台完成实验验证,结果表明,电流预测器对负载突变、速度突变等动态工况具有良好鲁棒性,提出的诊断方法可分别在0.65、0.9 ms内完成间歇性故障出现时间与消失时间的检测,且可准确辨识传感器间歇性故障的早期、中期、晚期阶段,实现了传感器间歇性故障严重程度的评价。

结合随机矩阵理论和张量分解的非线性导航大数据异常识别

为了提高导航大数据的精度,需要识别其中存在的异常数据。为了提升非线性导航大数据异常识别精度和效率,提出了基于张量分解的非线性导航大数据异常识别方法。首先,对非线性导航大数据进行张量分解,采用滑动矩形窗划分数据获得若干个时窗段,然后引入db4小波多尺度分解各窗内数据,获得尺度不同的小波系数,并利用重构小波系数张量代替缺失数据,完成数据缺失值填补,提高数据完整度;其次,将互信息作为度量标准,建立数据的互信息矩阵,对互信息矩阵中的元素展开规范化和中心化处理,通过奇异值分解获得数据特征;再次,引入随机矩阵理论对特征展开优化选择,计算导航大数据特征的重要度,获得高精度的数据特征;最后,建立孤立树,通过孤立树给出数据特征的异常得分,以此完成非线性导航大数据的异常识别。实验结果表明,所提方法的缺失值填补精度保持在0.9以上,特征提取覆盖率达到86.3%,特征冗余度低于6.12%,异常识别精度G-mean值高于60%,识别时间低于8 s,有效提升了非线性导航大数据的特征提取精度、识别精度及识别效率。

应用非线性KNN数据搜索的三维叠前自由表面多次波预测

自由表面多次波预测(SRMP)是自由表面多次波消除(SRME)以及成像的重要环节。SRME技术尽管有效,但理论上需要规则而密集的地震数据采集方式。然而实际炮点、检波点空间分布稀疏,地震数据不能满足SRME理论要求,常规的做法是在SRME之前将地震数据规则化。为了避免数据规则化环节,首先建立索引数据树管理三维叠前地震数据,并采用基于树形数据结构的非线性K近邻算法(KNN)从地震数据中实时搜索两道近似地震数据;然后利用动校—反动校消除实时搜索得到的近似地震道与实际地震道之间的旅行时误差;由以上两步获得单道孔径内任意向下反射点(DRP)所需要的两道地震数据用于SRMP。单道孔径内任意DRP均可由SRMP预测对应的多次波模型道,叠加所有DRP对应的预测结果可获得该道稳定的多次波模型数据。将该方法用于扩展的三维Pluto模型数据,结果表明该方法能有效预测三维自由表面多次波,从而保证高质量的自由表面多次波衰减结果。实际地震数据的应用证明了方法的实用性。

基于tSNE-LSTM算法的工业预测模型

随着工业生产的复杂性和规模的增加,准确的工业预测模型对于提高生产效率和降低成本至关重要。为此,提出一种基于tSNE-LSTM算法的工业预测模型来预测工业生产过程的温度。将t-SNE应用于数据降维和特征提取,然后使用LSTM进行序列建模和预测。该模型结合了t-SNE降维和LSTM循环神经网络的优势,能够有效地捕捉时间序列数据的非线性动态特征,并进行准确预测。在实际工业数据集上的实验验证结果表明,该模型在工业预测任务中具有较高的准确性和鲁棒性。

Damage Identification under Incomplete Mode Shape Data Using Optimization Technique Based on Generalized Flexibility Matrix

A generalized flexibility–based objective function utilized for structure damage identification is constructed for solving the constrained nonlinear least squares optimized problem. To begin with, the generalized flexibility matrix (GFM) proposed to solve the damage identification problem is recalled and a modal expansion method is introduced. Next, the objective function for iterative optimization process based on the GFM is formulated, and the Trust-Region algorithm is utilized to obtain the solution of the optimization problem for multiple damage cases. And then for computing the objective function gradient, the sensitivity analysis regarding design variables is derived. In addition, due to the spatial incompleteness, the influence of stiffness reduction and incomplete modal measurement data is discussed by means of two numerical examples with several damage cases. Finally, based on the computational results, it is evident that the presented approach provides good validity and reliability for the large and complicated engineering structures.