Testing for co‐integration and nonlinear adjustment in a smooth transition error correction model

This article introduces a testing procedure for cointegration and nonlinear adjustment in a smooth transition vector error correction model. To overcome the unidentified parameters problem under the null of no‐cointegration, the Wald statistic is optimized over the unidentified parameter space. The asymptotic distribution of the test statistic is shown to be non‐standard but nuisance parameter‐free and hence critical values are obtained by simulations, Simulations show that the proposed test outperforms the alternatives in small sample sizes both in terms of size and power. Application to the exchange rate‐monetary fundamentals relationship show that the proposed test works considerably well. This article also finds that nonlinear adjustment dynamics are symmetric for some currencies and therefore the speed of adjustment depends on the size of the deviations and is asymmetric for others, hence, the adjustment dynamics depend not only on the size but also on the sign of the deviations.     

PARAMETER IDENTIFICATION AND RELATED PROBLEMS FOR THE DYNAMIC MATRIX CONTROL MODEL

The parameter identification and related problems for the Dynamic Matrix Control model are studied in this work. A recursive algorithm is employed, and a modified version of it equipped with a useful stopping rule is proposed. The probabilistic approach is taken and the stochastic aspects of the problem is emphasized. It is demonstrated that the algorithm is consistent and possesses certain nice properties. The parameter set reduction problem has also been addressed thoroughly. By using the Kolmogorov-Smirnov statistic, a systematic procedure of hypothesis testing is developed. Various simulation results are demonstrated.     

Nonlinear dimension reduction based neural modeling for distributed parameter processes

Many chemical processes are nonlinear distributed parameter systems with unknown uncertainties. For this class of infinite-dimensional systems, the low-order model identification from process data is very important in practice. The dimension reduction with a principal component analysis (PCA) is only a linear approximation for nonlinear problem. In this study, a nonlinear dimension reduction based low-order neural model identification approach is proposed for nonlinear distributed parameter processes. First, a nonlinear principal component analysis (NL-PCA) network is designed for the nonlinear dimension reduction, which can transform the high-dimensional spatio-temporal data into a low-dimensional time domain. Then, a neural system can be easily identified to model this low-dimensional temporal data. Finally, the spatio-temporal dynamics can be reproduced using the nonlinear time/space reconstruction. The simulations on a typical nonlinear transport-reaction process show that the proposed approach can achieve a better performance than the linear PCA based modeling approach.     

Testing for structural change of AR model to threshold AR model

The purpose of this article is to develop the likelihood ratio test for the structural change of an AR model to a threshold AR model. It is shown that the log‐likelihood ratio test converges to the maxima of a two‐parameter Gaussian process in distribution. This limiting distribution is novel and we tabulate the critical values. Some simulations are carried out to examine the finite‐sample performance of this test statistic. This article also includes a weak convergence of a two‐parameter marked empirical process, which is of independent interest.     

On the Use of Online Reparametrization in Automated Platforms for Kinetic Model Identification

Parameter estimation algorithms integrated in automated platforms for kinetic model identification are required to solve two optimization problems: i) a parameter estimation problem given the available samples; ii) a model‐based design of experiments problem to select the conditions for collecting future samples. These problems may be ill‐posed, leading to numerical failures when optimization routines are applied. In this work, an approach of online reparametrization is introduced to enhance the robustness of model identification algorithms towards ill‐posed parameter estimation problems.     

Nonlinear system identification and model reduction using artificial neural networks

We present a technique for nonlinear system identification and model reduction using artificial neural networks (ANNs). The ANN is used to model plant input–output data, with the states of the model being represented by the outputs of an intermediate hidden layer of the ANN. Model reduction is achieved by applying a singular value decomposition (SVD)-based technique to the weight matrices of the ANN. The sequence of state values is used to convert the model to a form that is useful for state and parameter estimation. Examples of chemical systems (batch and continuous reactors and distillation columns) are presented to demonstrate the performance of the ANN-based system identification and model reduction technique.     

A modified Gay–Berne model for liquid crystal molecular dynamics simulation

We present a modified Gay–Berne (GB) potential, in which the attraction force can be tuned by adjusting one parameter only. An attractive parameter Ps is introduced for describing the strength of attractive force relative to its repulsive counterpart between large particles. Using the proposed model, the phase transition phenomena of liquid crystal molecules as the density varies from 0.32 to 0.24, have been investigated. Simulation results are compared with the results of using traditional GB potential. The phases and phase transitions observed by using different attractive parameters show the importance of studying the variation of attractive forces, which resulted in dramatic different phase transition phenomena.     

An alternative method for parameter identification from temperature programmed reduction (TPR) data

The experimental method of temperature programmed reduction (TPR) for the investigation of gas-solid reactions is well established and widely used since the 1970s. With regard to the temporal and financial effort for TPR measurements, the quantitative model-based analysis of the data is not adequately developed. The TPR data analysis is comprised of two aspects: a discrete model identification and a continuous parameter optimisation. In this contribution, a general model for TPR experiments is introduced and a strategy for the model identification is proposed. This results in a large number of continuous optimisation problems which can be solved very efficiently by a proposed optimisation algorithm that is especially tailored for the problem at hand. The applicability of the method is demonstrated using TPR measurements of iron oxide in hydrogen gas.     

Elastoplastic coupling to model cold ceramic powder compaction

The simulation of industrial processes involving cold compaction of powders allows for the optimization of the production of both traditional and advanced ceramics. The capabilities of a constitutive model previously proposed by the authors are explored to simulate simple forming processes, both in the small and in the large strain formulation. The model is based on the concept of elastoplastic coupling – providing a relation between density changes and variation of elastic properties – and has been tailored to describe the transition between a granular ceramic powder and a dense green body. Finite element simulations have been compared with experiments on an alumina ready-to-press powder and an aluminum silicate spray-dried granulate. The simulations show that it is possible to take into account friction at the die wall and to predict the state of residual stress, density distribution and elastic properties in the green body at the end of the forming process.     

A random process asperity model for adhesion between rough surfaces

A simple asperity model using random process theory is developed in the presence of adhesion, using the Derjaguin, Muller and Toporov model for each individual asperity. A new adhesion parameter is found, which perhaps improves the previous parameter proposed by Fuller and Tabor which assumed identical asperities – the model in all his variants for the radius always gives a finite pull-off force, as in Fuller and Tabor, and contrary to the exponential asperity height distribution, where the force is either always compressive, or always tensile. It is shown that a model with spheres having a radius only dependent on height is a reasonable approximation with respect to models having also a distribution of radius curvatures – the three models differ considerably, as opposed to the adhesionless case where these details did not matter. The important surface parameters in the theory determining the pull-off force are the three moments m₀, m₂, m₄. The asymptotic form of the model at large separation is solved in closed form. As the theoretical pull-off of aligned asperities having the same radius (the average value) increases with the square root of the Nayak bandwidth of the roughness, and as asperity models are known to describe less well the surface at large bandwidth parameters, the limit behavior at large bandwidths remains uncertain.     

Kissing bond detection in structural adhesive joints using nonlinear dynamic characteristics

In this paper, the effect of kissing bond on nonlinear dynamic behavior of structures with flexible adhesive joint is investigated. Bilinear characteristic due to opening and closing in kissing bond region results in nonlinear dynamic behavior of the structure such as harmonic distortion in response to harmonic excitation. So, the higher-order harmonics can be considered as Nonlinear Damage Indicator Functions (NDIF) for the purpose of damage identification. A two-dimensional (2D) finite element model of a beam connected to a rigid support via a flexible adhesive layer is used to investigate the efficiency of the proposed NDIFs in kissing bond detection. Kissing bond is introduced to the model via contact elements. NDIFs are extracted for the finite element model using single tone stepped-sine test simulation. Parameters such as amplitude of excitation, size and location of kissing bond region as well as friction between kissing surfaces, are studied. The results proved that the NDIFs are sensitive to the size and location of kissing bond. Consequently, in an experimental damage identification procedure, NDIFs can be used as an indicator of kissing bond type damages in adhesive joints.     

Combining non‐cointegration tests

The local power of many popular non‐cointegration tests has recently been shown to depend on a certain nuisance parameter. Depending on the value of that parameter, different tests perform best. This paper suggests combination procedures with the aim of providing meta tests that maintain high power across the range of the nuisance parameter. 1 We demonstrate the local power of the new meta tests to be in general almost as high as that of the most powerful of the underlying tests. When the underlying tests have similar power, the meta tests even appear more powerful than the best underlying test. At the same time, our new meta tests avoid the arbitrary decision which test to use if individual test results conflict. Moreover it avoids the size distortion inherent in separately applying multiple tests for cointegration to the same dataset. We use the new tests to investigate 286 datasets from published cointegration studies. There, in one‐third of all cases individual tests give conflicting results whereas our meta tests provide an unambiguous test decision.     

Estimation of a state-dependent model parameter

A numerical algorithm was developed which estimates a state-dependent model parameter on the basis of transient state observation data. The algorithm was presented for the problem of estimating the temperature dependence of thermal diffusivity in a one-dimensional heat equation. The estimation problem was converted into a finite dimensional optimization problem by the least-squares formulation and B-splines representation of the parameter. Numerical experiments were performed using simulated observation data as well as the actual observation data obtained in a heat conduction experiment on rubber compound layers. The performance of the algorithm was discussed in relation to the effect of the parameter representation scheme, the quality and quantity of the data.     

Analyzing the effect of composition,density, and the morphology of the “free” carbon phase on elastic moduli in silicon oxycarbide ceramics

We perform atomistic simulations to model structures and calculate elastic properties of silicon oxycarbide ceramics. We explore individual parameters – composition, density, carbon content – to disentangle mutual dependencies that are difficult to separate in experimental studies. Each parameter is studied through dynamic simulations at finite temperatures for a wide range of temperatures. With multi-million atom models in simulation boxes as large as 40 nm, we reveal a hitherto “hidden” parameter: the morphology of the “free” carbon phase. Embedding, distribution, and interconnection of the carbon phase inside the amorphous matrix of SiCO severely impact the material's mechanical properties. As a consequence, we call for the development of new characterization techniques that will quantify the morphology of carbon in this and similar systems.     

基于改进差分进化算法的质子交换膜燃料电池模型参数优化识别

质子交换膜燃料电池（proton exchange membrane fuel cell, PEMFC）模型中通常包含一些未知的难以确定的参数。为了有效确定这些参数,提出一种基于概率选择模型的改进差分进化算法。该概率选择模型为进化群体中的每个个体分配关于个体优劣性能的选择概率,然后基于该选择概率选择优秀的个体参与变异和交叉操作。为了验证改进算法的有效性,首先将其用于求解标准测试函数,实验结果表明,基于概率选择模型的变异、交叉操作能显著提高差分进化算法的收敛速度和求解精度。然后将改进算法用于质子交换膜燃料电池模型最优参数识别问题,得到的仿真结果和实验测试数据之间具有较高的拟合精度,表明本文改进算法是一种有效的求解系统模型参数优化识别的方法。     

Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simulta-neous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus al ows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization (AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.     

Iterative identification of output error model for industrial processes with time delay subject to colored noise

To dealwith colored noise and unexpected load disturbance in identification of industrial processes with time delay, a bias-eliminated iterative least-squares (ILS) identification method is proposed in this paper to estimate the output error model parameters and time delay simultaneously. An extended observation vector is constructed to establish an ILS identification algorithm. Moreover, a variable forgetting factor is introduced to enhance the convergence rate of parameter estimation. For consistent estimation, an instrumental variable method is given to deal with the colored noise. The convergence and upper bound error of parameter estimation are analyzed. Two illustrative examples are used to show the effectiveness and merits of the proposed method.     

Nonlinear model of pneumatic conveying dryer for economic process control

Abstract

This paper presents a nonlinear dynamic model, suitable for economic process control of pneumatic conveying dryer for drying of food grains. The dynamic model is developed by reshaping the process equations derived for the batch drying, dilute phase, and a negative-pressure conveying system. The dynamic model parameters are identified by numerically solving a nonlinear least squares optimization problem, subject to a set of differential and algebraic equality constraints that describe the system dynamics and bounds in the parameters. A detailed parametric uncertainty and sensitivity analysis are performed providing valuable insight into the influence of critical model parameters on observables, the interplay among various parameter-state-measured disturbances, and quantifying uncertainties in the model. Further, different process economic performance and product quality indicator of uncertain dryer model are studied. The model validation study as performed with the underlying process shows a very good agreement in understanding necessary dynamic characteristics and interplay between the various parameter of interest.     

平面正交各向异性体材料参数识别算法及软件设计

基于边界元法,本文提出了平面正交各向异性体材料参数识别算法.通过构造以测量位移与边界元计算的相应位移之差的平方和作为目标函数,采用Levenberg-Marquardt方法迭代极小化目标函数,从而把参数识别问题转化为极小化目标函数的问题.同时在识别过程中考虑了测量位移含正态分布的误差,研究测量误差对参数识别结果的影响.算例表明本文提出的参数识别方法是有效的.