NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS

In the industrial process situation, principal component analysis (PCA) is a general method in data reconciliation.However, PCA sometime is unfeasible to nonlinear feature analysis and limited in application to nonlinear industrial process.Kernel PCA (KPCA) is extension of PCA and can be used for nonlinear feature analysis.A nonlinear data reconciliation method based on KPCA is proposed.The basic idea of this method is that firstly original data are mapped to high dimensional feature space by nonlinear function, and PCA is implemented in the feature space.Then nonlinear feature analysis is implemented and data are reconstructed by using the kernel.The data reconciliation method based on KPCA is applied to ternary distillation column.Simulation results show that this method can filter the noise in measurements of nonlinear process and reconciliated data can represent the true information of nonlinear process.

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 Dimensionality Reduction and Data Visualization:A Review

维数减小和数据可视化是在模式识别的有用、重要的过程。许多技术在最近的年里被开发了。自我组织的地图(SOM ) 能是为这个目的一个有效方法。这篇论文在这考察最近的进展区域和相关途径(MDS ) ，非线性的 PCA，主要 manifolds，以及连接象多维的可伸缩那样，可视化导致了 SOM (ViSOM ) ，与这些途径。SOM 被显示生产一使量子化，质的可伸缩并且当时 ViSOM 一量或公制可伸缩并且接近主要弄弯 / 出现。SOM 能也被认为是概括 MDS 由形成在他们之间的拓扑的印射联系二个公制的空格。在象 ViSOM， Isomap， LLE，和 eigenmap 那样的各种各样的最近建议的技术之中的关系被讨论并且比较。关键词维数减小 - 非线性的数据设计 - 多维的可伸缩 - 自我组织的地图 - 非线性的 PCA - 校长歧管殷虎军四年级生在曼彻斯特大学的讲师，电、电子的工程的学校。他收到了 B.Eng， M.Sc。从来自在 1983 的约克的大学的东南大学和博士， 1986 和 1996 分别地。他的研究兴趣包括神经网络，自我组织的系统特别地，模式识别，图象处理和生物信息学。殷博士仔细看，扩大并且适用并且在最后十年里广泛地联系了话题(主要 manifolds 和数据可视化) 并且建议了包括贝叶斯的 SOM 和 ViSOM 的很多延期自我组织的地图(SOM ) 。他为超过二十个国际会议在计划委员会上服役了。他是为很多个会议组织和计划委员会主席和一般主席，包括，自我组织的地图的国际专题讨论会( WSOM 鈥? 1 )，设计并且学习自动化的聪明的数据上的国际会议(理想的鈥?3理想鈥? 7 )，并且神经网络上的国际会议( ISNN 鈥? 4-ISNN 鈥? 6 )。他坐在 WSOM 系列的指导委员会上。他是神经网络的一个客人编辑：在在自我组织的地图的新开发的 2002 特刊，在二本另外的国际杂志上的二期另外的特刊之中。他收到了从 EPSRC， BBSRC 和 DTI 资助的研究。殷博士出版了超过 90 篇考察同伴的文章。他是 IEEE 的一个高级成员和 EPSRC 同伴评论学院的一个成员。他也是一个常规裁判员因为 EPSRC， BBSRC 和皇家学会同意建议，香港研究资助会议和发展委员会。他是神经网络和神经系统的国际杂志的编委的一个成员上的 IEEE 交易的一个伙伴编辑。

A Novel Robust Nonlinear Dynamic Data Reconciliation

在一个变量的孤立点将在数据和解(医生) 涂另外的大小的评价。在这篇文章，一个新奇柔韧的方法为非线性的动态数据和解被建议，到在这个方法介绍的医生的结果上减少孤立点的影响在常规最少平方的客观功能的一个惩罚功能矩阵，为正常大小为孤立点和大重量分配小重量。避免数据信息的损失，元素明智的 Mahalanobis 距离被建议作为向量明智的距离上的改进，工作构造惩罚矩阵。测量错误的关联也在这篇文章被考虑。方法由构造惩罚重量矩阵介绍柔韧的统计理论进常规最不方形的评估者并且得到不仅好坚韧性而且简单计算。一个连续搅动的坦克反应堆的模拟，验证建议算法的有效性。

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 hy-perbolic systems with the boundary condition satisfying the dissipative condition aboutdiscontinuous data in higher dimension spaces, establishes the local existence theorem byusing the method of a prior estimates, and obtains the structure of singularities of thesolutions 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.

Guaranteed cost sampled-data control for uncertain nonlinear time-varying delay system

The robust guaranteed cost sampled-data control was studied for a class of uncertain nonlinear systems with time-varying delay. The parameter uncertainties are time-varying norm-bounded and appear in both the state and the input control matrices. By applying an input delay approach, the system was transformed into a continuous time-delay system. Attention was focused on the design of a robust guaranteed cost sampled-data control law which guarantees that the closed-loop system is asymptotically stable and the quadratic performance index is less than a certain bound for all admissible uncertainties. By applying Lyapunov stability theory, the theorems were derived to provide sufficient conditions for the existence of robust guaranteed cost sampled-data control law in the form of linear matrix inequalities (LMIs), especially an optimal state-feedback guaranteed cost sampled-data control law which ensures the minimization of the guaranteed cost was given. The effectiveness of the proposed method was illustrated by a simulation example with the asymptotically stable curves of system state under the initial condition of x(0)=[0.679 6 0].

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.

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.

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.

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.

变系数部分非线性模型的分位数回归估计

研究纵向数据缺失下变系数部分非线性分位数回归模型的估计问题.利用逆概率加权法结合分位数回归给出参数估计和非参估计;在一定条件下,证明了所给估计量的渐近正态性;通过数值模拟,验证了所提方法的有效性.

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.

物理信息神经网络求解五阶emKdV方程的正反问题

该文利用物理信息神经网络(PINNs)对扩展的五阶mKdV(emKdV)方程的正反问题进行求解,并对孤子的动力学行为进行分析、模拟.针对正问题,选用双曲正切函数tanh作为激活函数求解方程的一、二、三孤子解,并将PINNs方法求得的数据驱动解与借助简化的Hirota方法给出的方程精确解进行比较,一孤子解的精度为O(10^(-4)),二、三孤子解的精度为O(10^(-3)).针对反问题,分别由一、二、三孤子解的数据进行驱动求解方程的两个待定系数,并在不同的噪声下探究算法的鲁棒性.当在训练数据中加入1%的初始噪声或观测噪声时,待求系数的预测精度可分别达到O(10^(-3))和O(10^(-2));当加入3%的初始噪声或观测噪声时,预测精度依然可以达到O(10^(-2));由实验数据分析可知观测噪声对PINNs模型的影响要略大于初始噪声.

一种宽带非线性信道校正方法

针对高速数传解调器接收宽带高阶调制信号时因信道非线性导致的性能恶化问题,提出一种基于两层自适应均衡网络的非线性信道校正方法。该方法先利用递归最小二乘法获得Volterra非线性前置校正系数,再利用自适应基带均衡器消除信道的剩余影响,以提升解调器的捕获跟踪能力,并在高速数传解调器工程应用中实现2.4 Gb/s 16QAM信号的稳定接收。

THE STATE SPACE RECONSTRUCTION TECHNOLOGY OF DIFFERENT KINDS OF CHAOTIC DATA OBTAINED FROM DYNAMICAL SYSTEM

Certain deterministic nonlinear systems may show chaotic behavior.We consider the motion of qualitative information and the practicalities of extractinga part from chaotic experimental data.Our approach based on a theorem of Takensdraws on the ideas from the generalized theory of information known as singular sys-tem analysis.We illustrate this technique by numerical data from the chaotic regionof the chaotic experimental data.The method of the singular-value decompositionis used to calculate the eigenvalues of embedding space matrix.The correspondingconcrete algorithm to calculate eigenvectors and to obtain the basis of embeddingvector space is proposed in this paper.The projection on the orthogonal basis gener-ated by eigenvectors of timeseries data and concrete paradigm are also provided here.Meanwhile the state space reconstruction technology of different kinds of chaotic dataobtained from dynamical system has also been discussed in detail.

Ensemble-based Kalman Filters in Strongly Nonlinear Dynamics

This study examines the effectiveness of ensemble Kalman filters in data assimilation with the strongly nonlinear dynamics of the Lorenz-63 model, and in particular their use in predicting the regime transition that occurs when the model jumps from one basin of attraction to the other. Four configurations of the ensemble-based Kalman filtering data assimilation techniques, including the ensemble Kalman filter, ensemble adjustment Kalman filter, ensemble square root filter and ensemble transform Kalman filter, are evaluated with their ability in predicting the regime transition (also called phase transition) and also are compared in terms of their sensitivity to both observational and sampling errors. The sensitivity of each ensemble-based filter to the size of the ensemble is also examined.

Empirical data decomposition and its applications in image compression

A nonlinear data analysis algorithm, namely empirical data decomposition (EDD) is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation EBCOT . Simulation results show that EDD is more suitable for non-stationary image data compression.