Home About us Contact
|
Home About us Contact | ||
|
|
||
Model Identification (model + identification)
Selected AbstractsDifferential Equation Modeling of HIV Viral Fitness Experiments: Model Identification, Model Selection, and Multimodel InferenceBIOMETRICS, Issue 1 2009Hongyu Miao Summary Many biological processes and systems can be described by a set of differential equation (DE) models. However, literature in statistical inference for DE models is very sparse. We propose statistical estimation, model selection, and multimodel averaging methods for HIV viral fitness experiments in vitro that can be described by a set of nonlinear ordinary differential equations (ODE). The parameter identifiability of the ODE models is also addressed. We apply the proposed methods and techniques to experimental data of viral fitness for HIV-1 mutant 103N. We expect that the proposed modeling and inference approaches for the DE models can be widely used for a variety of biomedical studies. [source] Model identification in presence of incomplete information by generalized principal component analysis: Application to the common and differential responses of Escherichia coli to multiple pulse perturbations in continuous, high-biomass density cultureBIOTECHNOLOGY & BIOENGINEERING, Issue 4 2009Daniel V. Guebel Abstract In a previous report we described a multivariate approach to discriminate between the different response mechanisms operating in Escherichia coli when a steady, continuous culture of these bacteria was perturbed by a glycerol pulse (Guebel et al., 2009, Biotechnol Bioeng 102: 910,922). Herein, we present a procedure to extend this analysis when multiple, spaced pulse perturbations (glycerol, fumarate, acetate, crotonobetaine, hypersaline plus high-glycerol basal medium and crotonobetaine plus hypersaline basal medium) are being assessed. The proposed method allows us to identify not only the common responses among different perturbation conditions, but to recognize the specific response for a given stimulus even when the dynamics of the perturbation is unknown. Components common to all conditions are determined first by Generalized Principal Components Analysis (GPCA) upon a set of covariance matrices. A metrics is then built to quantify the similitude distance. This is based on the degree of variance extraction achieved for each variable along the GPCA deflation processes by the common factors. This permits a cluster analysis, which recognizes several compact sub-sets containing only the most closely related responsive groups. The GPCA is then run again but is restricted to the groups in each sub-set. Finally, after the data have been exhaustively deflated by the common sub-set factors, the resulting residual matrices are used to determine the specific response factors by classical principal component analysis (PCA). The proposed method was validated by comparing its predictions with those obtained when the dynamics of the perturbation was determined. In addition, it showed to have a better performance than the obtained with other multivariate alternatives (e.g., orthogonal contrasts based on direct GPCA, Tucker-3 model, PARAFAC, etc.). Biotechnol. Bioeng. 2009; 104: 785,795 © 2009 Wiley Periodicals, Inc. [source] Self-tuning PID controller using , -model identificationINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2002Vladimír Bobál Abstract This contribution presents an application of a self-tuning digital PID controller for process control modelled by , -models. The process is identified by the regression (ARX) model using the recursive least-squares method (RLSM) with LD decomposition and applied directional forgetting. Controller synthesis is designed on the basis of a modified Ziegler,Nichols criterion for digital PID control loops. The ultimate (critical) proportional gain and period of oscillations have been derived for the second-order , -model. Control results obtained using digital PID controller on the basic ,-models and z -models are compared. Copyright © 2002 John Wiley & Sons, Ltd. [source] Models of Quality-Adjusted Life Years when Health Varies Over Time: Survey and AnalysisJOURNAL OF ECONOMIC SURVEYS, Issue 2 2006Kristian Schultz Hansen Abstract., Quality-adjusted life year (QALY) models are widely used for economic evaluation in the health care sector. In the first part of the paper, we establish an overview of QALY models where health varies over time and provide a theoretical analysis of model identification and parameter estimation from time trade-off (TTO) and standard gamble (SG) scores. We investigate deterministic and probabilistic models and consider five different families of discounting functions in all. The second part of the paper discusses four issues recurrently debated in the literature. This discussion includes questioning the SG method as the gold standard for estimation of the health state index, re-examining the role of the constant-proportional trade-off condition, revisiting the problem of double discounting of QALYs, and suggesting that it is not a matter of choosing between TTO and SG procedures as the combination of these two can be used to disentangle risk aversion from discounting. We find that caution must be taken when drawing conclusions from models with chronic health states to situations where health varies over time. One notable difference is that in the former case, risk aversion may be indistinguishable from discounting. [source] Regularized semiparametric model identification with application to nuclear magnetic resonance signal quantification with unknown macromolecular base-lineJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2006Diana M. Sima Summary., We formulate and solve a semiparametric fitting problem with regularization constraints. The model that we focus on is composed of a parametric non-linear part and a nonparametric part that can be reconstructed via splines. Regularization is employed to impose a certain degree of smoothness on the nonparametric part. Semiparametric regression is presented as a generalization of non-linear regression, and all important differences that arise from the statistical and computational points of view are highlighted. We motivate the problem formulation with a biomedical signal processing application. [source] Behavioral modeling of GaN-based power amplifiers: Impact of electrothermal feedback on the model accuracy and identificationMICROWAVE AND OPTICAL TECHNOLOGY LETTERS, Issue 11 2009Roberto Quaglia Abstract In this article, we discuss the accuracy of behavioral models in simulating the intermodulation distortion (IMD) of microwave GaN-based high-power amplifiers in the presence of strong electrothermal (ET) feedback. Exploiting an accurate self-consistent ET model derived from measurements and thermal finite-element method simulations, we show that behavioral models are able to yield accurate results, provided that the model identification is carried out with signals with wide bandwidth and large dynamics. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 2789,2792, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24732 [source] Development and Experimental Identification of a Biomechanical Model of the Trunk for Functional Electrical Stimulation Control in ParaplegiaNEUROMODULATION, Issue 4 2008Ingenieur Michele Vanoncini ABSTRACT Objectives., Theoretic modeling and experimental studies suggest that functional electrical stimulation (FES) can improve trunk balance in spinal cord injured subjects. This can have a positive impact on daily life, increasing the volume of bimanual workspace, improving sitting posture, and wheelchair propulsion. A closed loop controller for the stimulation is desirable, as it can potentially decrease muscle fatigue and offer better rejection to disturbances. This paper proposes a biomechanical model of the human trunk, and a procedure for its identification, to be used for the future development of FES controllers. The advantage over previous models resides in the simplicity of the solution proposed, which makes it possible to identify the model just before a stimulation session (taking into account the variability of the muscle response to the FES). Materials and Methods., The structure of the model is based on previous research on FES and muscle physiology. Some details could not be inferred from previous studies, and were determined from experimental data. Experiments with a paraplegic volunteer were conducted in order to measure the moments exerted by the trunk-passive tissues and artificially stimulated muscles. Data for model identification and validation also were collected. Results., Using the proposed structure and identification procedure, the model could adequately reproduce the moments exerted during the experiments. The study reveals that the stimulated trunk extensors can exert maximal moment when the trunk is in the upright position. In contrast, previous studies show that able-bodied subjects can exert maximal trunk extension when flexed forward. Conclusions., The proposed model and identification procedure are a successful first step toward the development of a model-based controller for trunk FES. The model also gives information on the trunk in unique conditions, normally not observable in able-bodied subjects (ie, subject only to extensor muscles contraction). [source] Identification of plastic material parameters with error estimationPROCEEDINGS IN APPLIED MATHEMATICS & MECHANICS, Issue 1 2005Jaan Unger In recent years, inverse analysis has become a common approach to typical engineering problems such as model identification. In this contribution, the inverse problem is discussed in light of taking experimental uncertainties into account. This involves in particular the propagation of experimental errors and the analysis of the sensitivity of the model response to variations in the model parameters to be determined. The method is applied to an elasto-viscoplastic material model which is used in the context of electromagnetic high-speed forming. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Conditional Lifetime Data Analysis Using the Limited Expected Value FunctionQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 3 2004John Quigley Abstract Much failure, and other event, data are commonly highly censored. Consequently this limits the efficacy of many statistical analysis techniques. The limited expected value (LEV) function presents an alternative way of characterizing lifetime distributions. In essence the LEV provides a means of calculating a truncated mean time to failure (MTTF) (or mean time before failure (MTBF) if appropriate) that is adjusted at each of the censoring times and so appears potentially suitable for dealing with censored data structures. In theory, the LEV has been defined for many standard distributions, however its practical use is not well developed. This paper aims to extend the theory of LEV for typical censoring structures to develop procedures that will assist in model identification as well as parameter estimation. Applications to typical event data will be presented and the use of LEV in comparison with a selection of existing lifetime distributional analysis will be made based on some preliminary research. Copyright © 2004 John Wiley & Sons, Ltd. [source] Perturbation signal design for neural network based identification of multivariable nonlinear systemsTHE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 1 2002Pankaj S. Kulkarni Abstract The paper focuses on issues in experimental design for identification of nonlinear multivariable systems. Perturbation signal design is analyzed for a hybrid model structure consisting of linear and neural network structures. Input signals, designed to minimize the effects of nonlinearities during the linear model identification for the multivariable case, have been proposed and its properties have been theoretically established. The superiority of the proposed perturbation signal and the hybrid model has been demonstrated through extensive cross validations. The utility of the obtained models for control has also been proved through a case study involving MPC of a nonlinear multivariable neutralization plant. On traite dans cet article de la problématique des plans expérimentaux pour la détermination des systèmes multivariés non linéaires. La conception des signaux de perturbation est analysée pour un modèle de structure hybride composée de structures à réseaux linéaires et neuronaux. Des signaux d'entrée, con,us pour minimiser les effets des non-linéarités lors de la détermination du modèle linéaire pour le cas multivarié, sont proposés et leurs propriétés sont établies de manière théorique. La supériorité du signal de perturbation et du modèle hybride proposés est démontrée par des validations croisées poussées. L'utilité des modèles obtenus pour le contr,le est également prouvée par une étude de cas faisant intervenir le MPC d'une installation de neutralisation multivariée non linéaires. [source] Online identification of nonlinear multivariable processes using self-generating RBF neural networksASIAN JOURNAL OF CONTROL, Issue 5 2010Karim Salahshoor Abstract This paper addresses the problem of online model identification for multivariable processes with nonlinear and time-varying dynamic characteristics. For this purpose, two online multivariable identification approaches with self-organizing neural network model structures will be presented. The two adaptive radial basis function (RBF) neural networks are called as the growing and pruning radial basis function (GAP-RBF) and minimal resource allocation network (MRAN). The resulting identification algorithms start without a predefined model structure and the dynamic model is generated autonomously using the sequential input-output data pairs in real-time applications. The extended Kalman filter (EKF) learning algorithm has been extended for both of the adaptive RBF-based neural network approaches to estimate the free parameters of the identified multivariable model. The unscented Kalman filter (UKF) has been proposed as an alternative learning algorithm to enhance the accuracy and robustness of nonlinear multivariable processes in both the GAP-RBF and MRAN based approaches. In addition, this paper intends to study comparatively the general applicability of the particle filter (PF)-based approaches for the case of non-Gaussian noisy environments. For this purpose, the Unscented Particle Filter (UPF) is employed to be used as alternative to the EKF and UKF for online parameter estimation of self-generating RBF neural networks. The performance of the proposed online identification approaches is evaluated on a highly nonlinear time-varying multivariable non-isothermal continuous stirred tank reactor (CSTR) benchmark problem. Simulation results demonstrate the good performances of all identification approaches, especially the GAP-RBF approach incorporated with the UKF and UPF learning algorithms. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] A robust method for the joint estimation of yield coefficients and kinetic parameters in bioprocess modelsBIOTECHNOLOGY PROGRESS, Issue 3 2009V. Vastemans Abstract Bioprocess model structures that require nonlinear parameter estimation, thus initialization values, are often subject to poor identification performances because of the uncertainty on those initialization values. Under some conditions on the model structure, it is possible to partially circumvent this problem by an appropriate decoupling of the linear part of the model from the nonlinear part of it. This article provides a procedure to be followed when these structural conditions are not satisfied. An original method for decoupling two sets of parameters, namely, kinetic parameters from maximum growth, production, decay rates, and yield coefficients, is presented. It exhibits the advantage of requiring only initialization of the first subset of parameters. In comparison with a classical nonlinear estimation procedure, in which all the parameters are freed, results show enhanced robustness of model identification with regard to parameter initialization errors. This is illustrated by means of three simulation case studies: a fed-batch Human Embryo Kidney cell cultivation process using a macroscopic reaction scheme description, a process of cyclodextrin-glucanotransferase production by Bacillus circulans, and a process of simultaneous starch saccharification and glucose fermentation to lactic acid by Lactobacillus delbrückii, both based on a Luedeking-Piret model structure. Additionally, perspectives of the presented procedure in the context of systematic bioprocess modeling are promising. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [source] | ||||||||||||||||