基于渐近法的废气氧传感器Hammerstein模型辨识

为了解决与发动机空燃比控制相关的废气氧(EG0)传感器精确建模问题,基于渐近(ASYM)法辨识了EGO传感器的Hammerstein模型。模型的非线性部分用静态实验数据拟合,动态线性部分的辨识分为三步。先估计一个高阶ARX模型,然后依据渐近准则(ASYC)找出最佳频率响应估计的模型阶次,再采用极大似然(ML)法估计降阶后的模型参数。通过残差分析、交叉验证和模型误差模型(MEM)测试,将得到的ASYM模型与输出误差(OE)模型和Box-Jenkins(BJ)模型进行比较。结果表明,基于ASYM法的Hammerstein模型能够更精确地捕获EGO传感器的频域动态特性,并且用ASYM法能够量化模型的频域误差上限以评价建模精度。

Galileo卫星导航系统广播NeQuick模型及其参数拟合

提出了基于IGRF模型的Galileo广播NeQuick模型及其参数拟合算法,解决了Galileo信号仿真中地理场景映射与地磁坐标下的电离层延迟修正参数拟合问题.应用IGRF模型,可计算出任意给定位置和时间点的地磁参数以及E层、F_1层、F_2层的电子密度,从而计算出Galileo电离层修正参数.仿真结果表明,该算法拟合的全球电离层延迟与IGS提供的实际观测值基本一致,仿真精度高于一般的经验电离层模型,实现了Galileo卫星信号的电离层延迟修正参数的精确仿真.

系统辨识(6):多新息辨识理论与方法

多新息辨识是系统辨识的一个重要分支.新息是能够改善参数估计精度或状态估计精度的有用信息.首先,详细讨论了线性回归模型的各种多新息辨识方法,包括多新息投影算法、多新息随机梯度算法、多新息遗忘梯度算法、变递推间隔多新息随机梯度算法、多新息最小二乘辨识方法、变递推间隔多新息最小二乘算法等;然后,给出了方程误差类系统、输出误差类系统、输入非线性系统的随机梯度辨识算法、多新息随机梯度算法和多新息最小二乘辨识算法;最后,简单说明了多新息辨识理论可以发展到多新息观测器和多新息卡尔曼滤波理论.

系统辨识(8):耦合辨识概念与方法

耦合辨识是系统辨识的一个重要分支,是新近发展和提炼形成的一种辨识概念,主要用于研究结构复杂的参数耦合线性和非线性多变量系统的辨识问题.辅助模型辨识思想、多新息辨识理论、递阶辨识原理、耦合辨识概念是本文作者提出的一些新的辨识研究思路、理念和方法,分别能够用于研究存在未知过程变量的不可测系统的辨识,能够提高辨识方法的收敛速度和参数估计精度,能够解决结构复杂、大规模多变量系统及参数耦合多变量系统的辨识问题、减小辨识算法的计算量.首先介绍多变量系统耦合辨识概念,在此基础上讨论多变量系统的几种(全)耦合最小二乘辨识方法、(全)耦合随机梯度辨识方法、部分耦合随机梯度辨识方法、部分耦合最小二乘辨识方法等,最后说明耦合辨识方法可推广用于有色噪声干扰多变量系统的辨识,并列出了一些多变量系统模型结构,阐述了耦合辨识概念可以结合辅助模型辨识思想、多新息辨识理论、递阶辨识原理、迭代搜索原理(梯度迭代、最小二乘迭代、牛顿迭代)等来研究线性或非线性多变量系统的辨识问题.

棕榈油供给冲击与国内植物油价格波动的动态关系分析

【目的】分析棕榈油供给量与国内主要植物油价格的互动关系,以及对于来自价格正、负向冲击下棕榈油的供给反应,为棕榈油进口管理提供理论依据。【方法】利用我国棕榈油月度进口量、棕榈油、豆油、菜籽油现货价格及马来西亚毛棕榈油现货价格数据,在协整-GARCH、误差修正-GARCH框架下进行估计分析,研究供给量与价格间的互动关系,并用线性和非线性Granger因果检验进行稳健性检验。【结果】我国棕榈油的加工厂商和贸易商重视过去较长一段时间积累的方差信息(ARCH效应),而不是上一期的预测方差(GARCH效应);相比于菜籽油,豆油对棕榈油价格的调整力较大;棕榈油供给量对价格的影响力较小,有关价格滞后二阶的好坏消息对供给量具有非对称性影响;棕榈油进口量和价格相互不是Granger原因。【建议】加强棕榈油进口管理、市场建设及库存变化监测,加深棕榈油全球定价机制的研究。

Iterative identification of discrete-time output-error model with time delay

The problem of discrete-time model identification of industrial processes with time delay was investigated.An iterative and separable method is proposed to solve this problem,that is,the rational transfer function model parameters and time delay are alternately fixed to estimate each other.The instrumental variable technique is applied to guarantee consistent estimation against measurement noise.A noteworthy merit of the proposed method is that it can handle fractional time delay estimation,compared to existing methods commonly assuming that the time delay is an integer multiple of the sampling interval.The identifiability analysis for time delay is addressed and correspondingly,some guidelines are provided for practical implementation of the proposed method.Numerical and experimental examples are presented to illustrate the effectiveness of the proposed method.

Facets of Uncertainty in Digital Elevation and Slope Modeling

This paper investigates the differences that result from applying different approaches to uncertainty modeling and reports an experimental examining error estimation and propagation in elevation and slope, with the latter derived from the former. It is confirmed that significant differences exist between uncertainty descriptors, and propagation of uncertainty to end products is immensely affected by the specification of source uncertainty.

Effects of coal prices on merchandise prices in China

Coal is the principal form of energy used in China. Hence, coal price variations are expected to have some influence on merchandise prices. Monthly data from January, 2002, to October, 2010, were used to construct a varying-parameter state space model, and an error correction model, to estimate the influence of coat prices on Chinese merchandise prices. The time lag and the dynamic relationship were determined from the data. A long term equilibrium relationship between coal price and the PPI, and the CPI, can be observed. The long term influence of coal price fluctuations on the PPI is 0.263%. The corresponding value for the CPI is 0.157%. The PPI shows an influence from coal price change in the first period of observation： by eight periods the influence is obvious, after which it diminishes. The effect of coal price change on the CPI is rather weak and has no long term memory. Analysis of variance shows a similar situation. The elas- ticity coefficient of coal prices on the CPI, or the PPI, fluctuates over the 2002-2004 period. From 2002 to 2007 the influence elasticity on the CPI declined and subsequently levelled off after 2009.

Cumulative prospect theory-based user equilibrium model with stochastic perception errors

The cumulative prospect theory(CPT) is applied to study travelers' route choice behavior in a degradable transport network. A cumulative prospect theory-based user equilibrium(CPT-UE) model considering stochastic perception error(SPE) within travelers' route choice decision process is developed. The SPE is conditionally dependent on the actual travel time distribution, which is different from the deterministic perception error used in the traditional logit-based stochastic user equilibrium. The CPT-UE model is formulated as a variational inequality problem and solved by a heuristic solution algorithm. Numerical examples are provided to illustrate the application of the proposed model and efficiency of the solution algorithm. The effects of SPE on the reference point determination, cumulative prospect value estimation, route choice decision and network performance evaluation are investigated.

广义货币供应量M2与狭义货币供应量M1、现金M0关系的实证研究

本文利用计量经济学的方法建立现金M0、狭义货币供应量M1和广义货币供应量M2之间的数量关系。所建立的VAR模型表明,三者之间的短期动态关系是波动的、而非平稳的。所建立误差修正ECM模型表明,M0、M1、M2三者之间的长期静态关系是稳定的。考虑到M0、M1对M2的影响具有滞后性的,我们建立了反映三者之间长期动态关系的ARDL模型。本文利用上述模型的预测结果与实际值比较,得到的误差VAR模型最大、ARDL模型最小。文章的结尾,针对上述建立的M0、M1、M2之间的数量关系,作出了总结,建议通过调节存款准备金、控制银行利率等手段控制M0、M1来间接调控M2。

Comparative Study of Response Surface Designs with Errors-in-Variables Model

This paper investigates the scaled prediction variances in the errors-in-variables model and compares the performance with those in classic model of response surface designs for three factors.The ordinary least squares estimators of regression coefficients are derived from a second-order response surface model with errors in variables.Three performance criteria are proposed.The first is the difference between the empirical mean of maximum value of scaled prediction variance with errors and the maximum value of scaled prediction variance without errors.The second is the mean squared deviation from the mean of simulated maximum scaled prediction variance with errors.The last performance measure is the mean squared scaled prediction variance change with and without errors.In the simulations,1 000 random samples were performed following three factors with 20 experimental runs for central composite designs and 15 for Box-Behnken design.The independent variables are coded variables in these designs.Comparative results show that for the low level errors in variables,central composite face-centered design is optimal;otherwise,Box-Behnken design has a relatively better performance.

Rank-Defect Adjustment Model for Survey-Line Systematic Errors in Marine Survey Net

In this paper,the structure of systematic and random errors in marine survey net are discussed in detail and the adjustment method for observations of marine survey net is studied,in which the rank_defect characteristic is discovered first up to now.On the basis of the survey_line systematic error model,the formulae of the rank_defect adjustment model are deduced according to modern adjustment theory.An example of calculations with really observed data is carried out to demonstrate the efficiency of this adjustment model.Moreover,it is proved that the semi_systematic error correction method used at present in marine gravimetry in China is a special case of the adjustment model presented in this paper.

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model

Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation （CNOP）-type initial errors and nonlinear forcing singular vector （NFSV）- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error （NFSV-type tendency error） represents the initial errors （model errors） that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model （perfect initial conditions） scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events.

Reparameterization of Single Difference and Undifferenced Kinematic GPS Positioning Models

A series of advantages of single difference (SD) and undifferenced (ZD) models are given as compared with the double difference (DD) model. However, rank defects exist in SD and ZD models. The reparameterization method is provided to resolve this rank defect problem by estimating some combinations of the unknowns rather than the unknowns themselves. The reparameterization of SD and ZD functional models is discussed in detail with their stochastic models. The theoretical confirmation of the equivalence of undifferenced and differenced models is described in a straightforward way. The relationship between SD and ZD residuals is given and verified for some special purposes, e.g. research on the stochastical properties of GPS observations.

Auxiliary error and probability density function based neuro-fuzzy model and its application in batch processes

This paper focuses on resolving the identification problem of a neuro-fuzzy model(NFM) applied in batch processes. A hybrid learning algorithm is introduced to identify the proposed NFM with the idea of auxiliary error model and the identification principle based on the probability density function(PDF). The main contribution is that the NFM parameter updating approach is transformed into the shape control for the PDF of modeling error. More specifically, a virtual adaptive control system is constructed with the aid of the auxiliary error model and then the PDF shape control idea is used to tune NFM parameters so that the PDF of modeling error is controlled to follow a targeted PDF, which is in Gaussian or uniform distribution. Examples are used to validate the applicability of the proposed method and comparisons are made with the minimum mean square error based approaches.

Numerical simulation of the hydrodynamics within octagonal tanks in recirculating aquaculture systems

A three-dimensional numerical model was established to simulate the hydrodynamics within an octagonal tank of a recirculating aquaculture system. The realizable k-e turbulence model was applied to describe the flow, the discrete phase model （DPM） was applied to generate particle trajectories, and the governing equations are solved using the finite volume method. To validate this model, the numerical results were compared with data obtained from a full-scale physical model. The results show that： （1） the realizable k-e model applied for turbulence modeling describes well the flow pattern in octagonal tanks, giving an average relative error of velocities between simulated and measured values of 18% from contour maps of velocity magnitudes; （2） the DPM was applied to obtain particle trajectories and to simulate the rate of particle removal from the tank. The average relative error of the removal rates between simulated and measured values was 11%. The DPM can be used to assess the self-cleaning capability of an octagonal tank; （3） a comprehensive account of the hydrodynamics within an octagonal tank can be assessed from simulations. The velocity distribution was uniform with an average velocity of 15 cm/s; the velocity reached 0.8 m/s near the inlet pipe, which can result in energy losses and cause wall abrasion; the velocity in tank corners was more than 15 cm/s, which suggests good water mixing, and there was no particle sedimentation. The percentage of particle removal for octagonal tanks was 90% with the exception of a little accumulation of 〈5 mm particle in the area between the inlet pipe and the wall. This study demonstrated a consistent numerical model of the hydrodynamics within octagonal tanks that can be further used in their design and optimization as well as promote the wide use of computational fluid dynamics in aquaculture engineering.

Artificial neural network approach for rheological characteristics of coal-water slurry using microwave pre-treatment

Detailed experimental investigations were carried out for microwave pre-treatment of high ash Indian coal at high power level(900 W) in microwave oven. The microwave exposure times were fixed at60 s and 120 s. A rheology characteristic for microwave pre-treatment of coal-water slurry(CWS) was performed in an online Bohlin viscometer. The non-Newtonian character of the slurry follows the rheological model of Ostwald de Waele. The values of n and k vary from 0.31 to 0.64 and 0.19 to 0.81 Pa·sn,respectively. This paper presents an artificial neural network(ANN) model to predict the effects of operational parameters on apparent viscosity of CWS. A 4-2-1 topology with Levenberg-Marquardt training algorithm(trainlm) was selected as the controlled ANN. Mean squared error(MSE) of 0.002 and coefficient of multiple determinations(R^2) of 0.99 were obtained for the outperforming model. The promising values of correlation coefficient further confirm the robustness and satisfactory performance of the proposed ANN model.

Processing Approach of Non-linear Adjustment Models in the Space of Non-linear Models

This paper investigates the mathematic features of non-linear models and discusses the processing way of non-linear factors which contributes to the non-linearity of a non-linear model. On the basis of the error definition,this paper puts forward a new adjustment criterion, SGPE.Last,this paper investigates the solution of a non-linear regression model in the non-linear model space and makes the comparison between the estimated values in non-linear model space and those in linear model space.

ANN model of subdivision error based on genetic algorithm

According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating precision.

Salting-out effect of ionic liquids on isobaric vapor-liquid equilibrium of acetonitrile-water system

This paper presents the vapor–liquid equilibrium(VLE) data of acetonitrile–water system containing ionic liquids(ILs) at atmospheric pressure(101.3 k Pa). Since ionic liquids dissociate into anions and cations, the VLE data for the acetonitrile + water + ILs systems are correlated by salt effect models, Furter model and improved Furter model. The overall average relative deviation of Furter model and improved Furter model is 5.43% and 4.68%, respectively. Thus the salt effect models are applicable for the correlation of IL containing systems. The salting-out effect theory can be used to explain the change of relative volatility of acetonitrile–water system.