AN OPTIMAL MODEL AND ITS IDENTIFICATION FOR THE THERMODYNAMICS OF KEROGEN EV

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动力学方程组中参数识别模型及其优化性质

本文建立了动力学方程组中参数优化识别的数学模型.这是个含有动态约束的泛函极小化问题.应用线性算子的连续性,证明了该问题最优解的存在性,即参数的可识别性.最后依据弱Gateaux微分,给出并证明达到最优解的一个必要条件.     

Prevalence of locally parameter identifiable systems

The problem of local parameter identifiability of an input–output system is considered. A set lf of systems is studied for which the property of local parameter identifiability holds for almost all values of input signals and parameters in both topological and metric senses. Sufficient conditions are pointed out under which the set LI contains a prevalent subset. The proof is based on the prevalent transversality theorem proved by Kaloshin. Systems are considered that are characterized by a family of (structural) parameters a and a control block. It is shown that if the dimension of the set of parameters a is large enough (the structure of the system is rich enough), then, generically, a system f _a belongs to the class lf for a set of parameters a having full measure.     

A Group-Theoretic Approach to Parameter Identifiability of PDE Systems

The contribution is devoted to the parameter identifiability problem of (nonlinear) PDE systems. Especially, we discuss the (local) identifiability of parameters along a trajectory. The analysis relies on a coordinate-free formulation for systems, including boundary conditions, and we motivate an approach by (Lie) transformation groups, whose success for PDE systems depends on a consequent extent to the accompanying boundary conditions. The (non-)identifiability of parameters is related to the (non-)existence of group generators, wherewith (local) conditions can be derived. (© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Estimation of the position and time of emission of a source

We consider the problem of the estimation of the position on the plane and of the moment of beginning of emission of a source by observations from K detectors on the plane. We propose the conditions of regularity and identifiability, which allow us to use two general theorems by Ibragimov and Khasminskii and to describe the asymptotic behavior of the maximum likelihood and Bayes estimators. Then we propose the construction of a linear estimator of unknown parameters and study its properties in slightly more general situation. Special attention is payed to condition of identifiability.

     

On the identifiability of measurement error in the bifurcating autoregressive model

Huggins and Staudte (1994) considered a mixed linear model for the analysis of cell lineage data and in models for the covariance structure which involved measurement error, it was not immediately clear that the parameters involved were identifiable. Whilst a numerical examination of the Hessian matrix at the estimated parameter values gave some reassurance, this was not theoretically satisfying. Here a matrix formulation of the robust estimating functions of Huggins (1993a, b) as applied in Huggins and Staudte (1994), which include the maximum likelihood estimating functions under the assumption of multivariate normality as a special case, is given along with a direct proof linking identifiability expressed in terms of the estimating functions with the information matrix or its analogue in more general settings. The resulting conditions on the estimating functions may then be checked globally using computer algebra, suggesting a method for establishing identifiability in mixed linear models in general.     

Local parameter identifiability for one class of hybrid systems

We consider the problem of local parameter identifiability for a hybrid system with components that are continuous and discrete in time. The set of observations is the vector-solution (depending on the parameter that is continuous in time) of the discrete component. Sufficient conditions of local parameter identifiability have been formulated using the earlier introduced notion of normalized separability of the set of parameters from the kernel of a special functional. An example where the condition of normed separability is reduced to some rank criterion is given.     

Computer Algebra Tests on Identifiability of Nonlinear Implicit Dynamical Systems

In this contribution criteria on identifiability of system parameters of nonlinear implicit dynamical systems, also denoted differential algebraic equation systems (DAEs), are presented. Two interesting questions can be posed in this context: We ask for identifiability along a given trajectory and the existence of a trajectory through a given point in the state space such that the parameters are identifiable along it. A computer algebra algorithm to test these properties is provided. As a special case systems in explicit form are considered and a mechanical example is shown as application. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

微生物批式流加发酵脉冲系统的参数辨识及稳定性

针对微生物批式流加发酵生产1,3-丙二醇的非线性脉冲系统,建立敏感参数的优化辨识模型(PDP),论述了模型解的性质、解与参量的关系以及辨识问题最优解的存在性.通过构造算法求得辨识问题最优解,并讨论了新参数下脉冲系统解的稳定性.     

On Huber's contaminated model

Huber's contaminated model is a basic model for data with outliers. This paper aims at addressing several fundamental problems about this model. We first study its identifiability properties. Several theorems are presented to determine whether the model is identifiable for various situations. Based on these results, we discuss the problem of estimating the parameters with observations drawn from Huber's contaminated model. A definition of estimation consistency is introduced to handle the general case where the model may be unidentifiable. This consistency is a strong robustness property. After showing that existing estimators cannot be consistent in this sense, we propose a new estimator that possesses the consistency property under mild conditions. Its adaptive version, which can simultaneously possess this consistency property and optimal asymptotic efficiency, is also provided. Numerical examples show that our estimators have better overall performance than existing estimators no matter how many outliers in the data.     

带有变量中误差的 Probit 回归模型

一、引言描述一事件概率与影响变量间关系的 Probit 回归与 Logit 回归皆为重要的非线性模型.Logit 模型1944年由 Berkson 提出 Probit 模型1860年 Fechner 提出.对影响变量进行考察常会出现误差,称为“变量中的误差”(FV),在线性模型中的 FV问题已有系统研究,近来又扩展到各种非线性模型中.本文讨论 FV-Probit 模型.     

Identifiability analysis of an epidemiological model in a structured population

We investigate the parameter identifiability problem for a system of nonlinear integro-partial differential equations of transport type, representing the spread of a disease with a long infectious but undetectable period in an individual population. After obtaining the expression of the model input-output relationships, we give sufficient conditions on the initial and boundary conditions of the system that guarantee the parameter identifiability on a finite time horizon. We finally illustrate our findings with numerical simulations.     

System Identifiability (Symbolic Computation) and Parameter Estimation (Numerical Computation)

A system identification based on physical laws often involves a parameter estimation. Before performing an estimation problem, it is necessary to investigate its identifiability. This investigation leads often to painful calculations. Generally, the numerical computation of the parameters does not use these calculus. In this contribution we propose least-squares methods to link identifiability approaches with numerical parameter estimation.     

Locally parameter identifiable systems are generic

The problem of local parameter identifiability of an ??input-output?? system is considered. It is shown that if the dimension of the parameter space is not higher than that of the space of output signals, then a generic system is locally parameter identifiable for almost all values of the input signal and parameter. We give an example which illustrates that if the number of parameters exceeds the dimension of the output signal, then the corresponding assertion ceases to be true.     

Identifiability of factor analysis: some results and open problems

Identifiability of the factor analysis model is discussed, and some recent results and their relation to previous work are surveyed. Sufficient conditions for local identifiability are presented. A counterexample shows that these conditions are not necessary in general. A simple counting rule for local identifiability of the factor analysis model is given generically. Finally some open problems are formulated.     

Variable selection in model-based discriminant analysis

A general methodology for selecting predictors for Gaussian generative classification models is presented. The problem is regarded as a model selection problem. Three different roles for each possible predictor are considered: a variable can be a relevant classification predictor or not, and the irrelevant classification variables can be linearly dependent on a part of the relevant predictors or independent variables. This variable selection model was inspired by a previous work on variable selection in model-based clustering. A BIC-like model selection criterion is proposed. It is optimized through two embedded forward stepwise variable selection algorithms for classification and linear regression. The model identifiability and the consistency of the variable selection criterion are proved. Numerical experiments on simulated and real data sets illustrate the interest of this variable selection methodology. In particular, it is shown that this well ground variable selection model can be of great interest to improve the classification performance of the quadratic discriminant analysis in a high dimension context.     

Robustness and convergence in the Lee–Carter model with cohort effects

Interest in cohort effects in mortality data has increased dramatically in recent years, with much of the research focused on extensions of the Lee–Carter model incorporating cohort parameters. However, some studies find that these models are not robust to changes in the data or fitting algorithm, which limits their suitability for many purposes. It has been suggested that these robustness problems may be the result of an unresolved identifiability issue. In this paper, after investigating systemically the robustness of cohort extensions of the Lee–Carter model and the convergence of the algorithms used to fit it to data, we demonstrate the existence of such an identifiability issue and propose an additional approximate identifiability constraint which solves many of the problems found.     

Identification of physical parameters in pressing of rapeseeds from the oil flux measurements

In this paper, we are interested in the study of an inverse problem that occurs during the pressing of rapeseeds, where some physical parameters influence on rapeseed oil extraction yield. Our objective is to identify the consolidation coefficient of the press cake, the inverse characteristic time of consolidation in press cake, and inside the rapeseed in order to increase the rapeseed oil extraction yield. Three questions will be addressed: the identifiability, the identification, and the stability of the inverse problem. Finally, we provide numerical results to confirm the theoretical results.     

Stochastic realization of a Gaussian stochastic control system

The stochastic realization problem is considered of representing a stationary Gaussian process as the observation process of a Gaussian stochastic control system. The problem formulation includes that the lastm components of the observation process form the Gaussian white noise input process to the system. Identifiability of this class of systems motivates the problem. The results include a necessary and sufficient condition for the existence of a stochastic realization. A subclass of Gaussian stochastic control systems is defined that is almost a canonical form for this stochastic realization problem. For a structured Gaussian stochastic control system an equivalent condition for identifiability of the parametrization is stated.The research of this paper is supported in part by the Commission of the European Communities through the SCIENCE Program by the projectSystem Identification with contract number SC1-CT92-0779.     

Inverse Problems for Multidimensional Heat Equations by Measurements at a Single Point on the Boundary

In this article we recover a coefficient in a multidimensional inverse problem for a heat equation. We show that a sequence of measurements taken at the same point on the boundary but at different times is enough to determine the coefficient uniquely. We provide an identifiability algorithm for both Dirichlet and Neumann lateral boundary conditions and we examine the smoothness of the recovered coefficient.