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Results for “parameter identification”

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At least 19 recordsLinked to original sources

PubMedDate not supplied

Parameter identification in coronary pressure flow models: a graphical approach.

The confident identification of parameters is important in the practical application of physiological models. However, the task of parameter identification is often complicated by interactions among parameters and by the fact that the sensitivity of the model to changes in a given parameter is generally a function of all the other parameters. Here we illustrate a graphical approach to parameter identification that allows the modeler to visualize the behavior of the model, the sensitivity functions, and certain functions characteristic of parameter interdependence. The visual display can be generated over any desired portion of parameter space. The technique is applied to a simple, four-parameter, myocardial pump model of the coronary circulation. The results indicate that over specified ranges of parameters, it is possible to distinguish among the four parameters of the model: the ratio of proximal-to-distal resistance, alpha; the overall resistance of the vascular bed, R; the compliance of the vascular bed, C; and a parameter, kappa, relating tissue pressure to left ventricular pressure. It was found that in order to identify all parameters uniquely, it was necessary to regress upon both coronary inflow and outflow.

Blood Pressure↗

PubMed2002

Linear phase correction models for synchronization: parameter identification and estimation of parameters.

Linear phase correction models for synchronized tapping and their stochastic properties are presented. In the most general form they include a central timer, a motor execution, and a phase correction mechanism that acts on the physical or the perceived asynchrony. A central issue of the article is how to identify and estimate the model parameters from the data. Monte Carlo simulations show serious problems of parameter interdependency.

Humans↗

PubMed2003

[Comparison of optimum, RSA and GLUE methods in parameter identification of a nonlinear environmental model].

Parameter identification plays a key role in environmental model application. The optimization method is one of the earliest and most widely used methods. However, as the parameters by optimization may not fully fit the observations, there is a risk that the errors may be enhanced in the decision-make stage. With this deficiency in consideration, the RSA and GLUE algorithms search for the feasible parameters not only to the optimum but also around the neighbors. The difference between RSA and GLUE is that the RSA accepts the estimated parameters equally as the candidates for application; while the GLUE keeps the difference among the parameters as measured by likelihood. In addition for parameter identification, both RSA and GLUE are efficient tools for global sensitivity analysis.

Environment↗

PubMed1991

Recursive parameter identification of constrained systems: an application to electrically stimulated muscle.

In the application of real-time identification methods for diagnosis or adaptive control of biomedical systems, there is often known model information that is ignored. Constraints on the allowable values of parameters, which may be based on physical considerations, are often neglected because the information does "fit" easily into commonly used parameter-identification algorithms. In this paper a method of incorporating constraints on model parameters is developed. This method is applicable to most recursive parameter-identification algorithms. It enforces linear equality constraints on identified parameters. The use of this method for the real-time identification of autoregressive moving-average-type time series models, subject to parameter constraints, is described in detail. These constraints may be time varying. At each time step, the parameter estimate obtained by a recursive least squares estimator is orthogonally projected onto the constraint surface. This simple idea, when appropriately executed, enhances the output prediction accuracy of estimated parameters. Using constraint information in this way is important when we do not wish to destroy a "natural" parameterization of the model (by an initial projection to incorporate equality constraints), or when we cannot use a single initial model simplification (because the constraints are time varying or involve inputs and outputs). Because it improves output prediction at future times, this method is advantageous for use in predictive adaptive controllers. The use of this algorithm is demonstrated in the identification of electrically stimulated quadriceps muscles in paraplegic human subjects, using percutaneous intramuscular electrodes. The nonlinear steady-state force versus pulsewidth recruitment characteristic of the electrode-muscle system is identified simultaneously with the input-output muscle response dynamics, using a Hammerstein-type model. Knowledge of the recruitment curve's shape is translated into constraints on the identified parameters. This information improves the experimental predictive quality of the identified model.

Adult↗

PubMed2006

Adaptive Q-S (lag, anticipated, and complete) time-varying synchronization and parameters identification of uncertain delayed neural networks.

In this paper, a new type of generalized Q-S (lag, anticipated, and complete) time-varying synchronization is defined. Adaptive Q-S (lag, anticipated, and complete) time-varying synchronization and parameters identification of uncertain delayed neural networks have been considered, where the delays are multiple time-varying delays. A novel control method is given by using the Lyapunov functional method. With this new and effective method, parameters identification and Q-S (lag, anticipated, and complete) time-varying synchronization can be achieved simultaneously. Simulation results are given to justify the theoretical analysis in this paper.

Action Potentials↗

PubMed2001

Dynamical model development and parameter identification for an anaerobic wastewater treatment process.

This paper deals with the development and the parameter identification of an anaerobic digestion process model. A two-step (acidogenesis-methanization) mass-balance model has been considered. The model incorporates electrochemical equilibria in order to include the alkalinity, which has to play a central role in the related monitoring and control strategy of a treatment plant. The identification is based on a set of dynamical experiments designed to cover a wide spectrum of operating conditions that are likely to take place in the practical operation of the plant. A step by step identification procedure to estimate the model parameters is presented. The results of 70 days of experiments in a 1-m(3) fermenter are then used to validate the model.

Bacteria, Anaerobic↗

PubMed2005

Temporal adaptability and the inverse relationship to sensitivity: a parameter identification model.

Following a prolonged period of visual adaptation to a temporally modulated sinusoidal luminance pattern, the threshold contrast of a similar visual pattern is elevated. The adaptive elevation in threshold contrast is selective for spatial frequency, may saturate at low adaptor contrast, and increases as a function of the spatio-temporal frequency of the adapting signal. A model for signal extraction that is capable of explaining these threshold contrast effects of adaptation is proposed. Contrast adaptation in the model is explained by the identification of the parameters of an environmental model: the autocorrelation function of the visualized signal. The proposed model predicts that the adaptability of threshold contrast is governed by unpredicted signal variations present in the visual signal, and thus represents an internal adjustment by the visual system that takes into account these unpredicted signal variations given the additional possibility for signal corruption by additive noise.

Adaptation, Ocular↗

PubMed2002

On-line parameter identification of systemic circulation using the delta operator.

To develop effective medical care with the artificial heart, we propose a new method, on-line parameter identification of the systemic circulation using the delta operator which can calculate the time-varying and unmeasured hemodynamics of the internal human body from some measured data: aortic pressure and total flow in real time. This method consists of first, a dynamic physiological model which is configured with the physiological parameters Ca (aortic compliance) and Rp (total peripheral resistance); and second, a system identification method using the delta operator. In the computer simulation study, we could confirm the effectiveness to identify the physiological parameters. In animal experiments with a left ventricular assist system, the physiological parameters, Ca = 1.8 (ml/mm Hg) and Rp = 0.8 (mm Hg s/ml), could be identified on-line.

Adaptation, Physiological↗

PubMed2005

Aorta in vivo parameter identification using an axial force constraint.

It was shown in a previous study by Stålhand et al. (2004) that both material and residual strain parameters for an artery can be identified noninvasively from an in vivo clinical pressure-diameter measurement. The only constraints placed on the model parameters in this previous study was a set of simple box constraints. More advanced constraints can also be utilized, however. These constraints restrict the model parameters implicitly by demanding the state of the artery to behave in a specified way. It has been observed in vitro that the axial force is nearly invariant to the pressure at the physiological operation point. In this paper, we study the possibility to include this behaviour as a constraint in the parameter optimization. The method is tested on an in vivo obtained pressure-diameter cycle for a 24-year-old human. Presented results show that the constrained parameter identification procedure proposed here can be used to obtain good results, and we believe that it may be applied to account for other observed behaviours as well.

Adult↗

PubMedDate not supplied

Simulating solute transport in a structured field soil: uncertainty in parameter identification and predictions.

Dual-permeability models have been developed to account for the significant effects of macropore flow on contaminant transport, but their use is hampered by difficulties in estimating the additional parameters required. Therefore, our objective was to evaluate data requirements for parameter identification for predictive modeling with the dual-permeability model MACRO. Two different approaches were compared: sequential uncertainty fitting (SUFI) and generalized likelihood uncertainty estimation (GLUE). We investigated six parameters controlling macropore flow and pesticide sorption and degradation, applying MACRO to a comprehensive field data set of bromide andbentazone [3-isopropyl-1H-2,1,3-benzothiadiazin-4(3H)-one-2,2dioxide] transport in a structured soil. The GLUE analyses of parameter conditioning for different combinations of observations showed that both resident and flux concentrations were needed to obtain highly conditioned and unbiased parameters and that observations of tracer transport generally improved the conditioning of macropore flow parameters. The GLUE "behavioral" parameter sets covered wider parameter ranges than the SUFI posterior uncertainty domains. Nevertheless, estimation uncertainty ranges defined by the 5th and 95th percentiles were similar and many simulations randomly sampled from the SUFI posterior uncertainty domains had negative model efficiencies (minimum of -3.2). This is because parameter correlations are neglected in SUFI and the posterior uncertainty domains were not always determined correctly. For the same reasons, uncertainty ranges for predictions of bentazone losses through drainflow for good agricultural practice in southern Sweden were 27% larger for SUFI compared with GLUE. Although SUFI proved to be an efficient parameter estimation tool, GLUE seems better suited as a method of uncertainty estimation for predictions.

Benzothiadiazines↗

PubMed2005

Methods for parameter identification in oscillatory networks and application to cortical and thalamic 600 Hz activity.

Directed information transfer in the human brain occurs presumably by oscillations. As of yet, most approaches for the analysis of these oscillations are based on time-frequency or coherence analysis. The present work concerns the modeling of cortical 600 Hz oscillations, localized within the Brodmann Areas 3b and 1 after stimulation of the nervus medianus, by means of coupled differential equations. This approach leads to the so-called parameter identification problem, where based on a given data set, a set of unknown parameters of a system of ordinary differential equations is determined by special optimization procedures. Some suitable algorithms for this task are presented in this paper. Finally an oscillatory network model is optimally fitted to the data taken from ten volunteers.

Cerebral Cortex↗

PubMed2006

Parameter identification and sedative sensitivity analysis of an agitation-sedation model.

Sedation administration and agitation management are fundamental activities in any intensive care unit. A lack of objective measures of agitation and sedation, as well as poor understanding of the underlying dynamics, contribute to inefficient outcomes and expensive healthcare. Recent models of agitation-sedation dynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. In this research, the agitation-sedation model parameters are identified using an integral-based fitting method developed in this work. Parameter variance is then analysed over 37 intensive care unit patients. The parameter identification method is shown to be effective and computationally inexpensive, making it suited to real-time clinical control applications. Sedative sensitivity, an important model parameter, is found to be both patient-specific and time-varying. However, while the variation between patients is observed to be as large as a factor 10, the observed variation in time is smaller, and varies slowly over a period of days rather than hours. The high fitted model performance across all patients show that the agitation-sedation model presented captures the fundamental dynamics of the agitation-sedation system. Overall, these results provide additional insight into the system and clinical dynamics of sedation management.

Computer Simulation↗

PubMed2004

Resonant frequency control for artificial heart using online parameter identification.

To develop effective medical care and therapeutic control using an artificial heart, a new control method has been developed. This new method can control the artificial heart effectively and can adapt to internal physiological behavior using measured physiological data; aortic pressure, aortic flow, and pump flow. This method consists of first, a second-order physiological model, which represents the internal physiological behavior by a mathematical equation; and second, an estimation method, which can identify the physiological parameters; aortic inertia, aortic resistance, aortic compliance, and peripheral resistance by a parameter identification method. It can then calculate the resonant frequency as the control signal for the artificial heart from the identified physiological model. To confirm the effectiveness, the proposed method was evaluated in a computer simulation study. This evaluation showed that the new method could estimate the physiological parameters and the resonant frequency within a 10% error. The impedance of the systemic circulation could also be reduced by this method.

Algorithms↗

PubMed2005

Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model.

Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. This paper introduces a model capable of capturing the essential glucose and insulin kinetics in patients from retrospective data gathered in an intensive care unit (ICU). The model uses two time-varying patient specific parameters for glucose effectiveness and insulin sensitivity. The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters. The method was tested on long-term blood glucose recordings from 17 ICU patients, producing 4% average error, which is within the sensor error. One-hour forward predictions of blood glucose data proved acceptable with an error of 2-11%. All identified parameter values were within reported physiological ranges. The parameter identification method is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods. These results verify the model's ability to capture long-term observed glucose-insulin dynamics in hyperglycemic ICU patients, as well as the fitting method developed. Applications of the model and parameter identification method for automated control of blood glucose and medical decision support are discussed.

Adult↗

PubMed1993

A parameter identification problem arising from a model of canalicular bile formation.

We develop a simple mathematical model for bile formation and analyze some features of the model that suggest the design for future physiological experiments. The mathematical model results in a boundary value problem for a system of functional differential equations depending on several physical parameters. From the observability of the boundary values we can identify, both qualitatively and quantitatively, some of these physical parameters. This identification then suggests physical experiments from which one could infer some of the bile transport phenomena that are not, at present, directly observable. The mathematical parameter identification problem is solved by converting the boundary value problem to a transition time problem for a quadratic system of ordinary differential equations on the plane where we are able to employ some special properties of quadratic systems in order to obtain a solution.

Animals↗

PubMed2002

The role of parameter identification in finite element contact analyses with reference to orthopaedic biomechanics applications.

A finite element model accounting for large sliding frictional contact requires, depending on the type of contact algorithm in use, the definition of many numerical parameters such as contact stiffness, convergence norm and tolerance, compenetration monitoring, over-relaxing factors, etc. All these parameters do not have a physical meaning and thus they cannot be measured experimentally. This makes their identification quite complex. The aim of this study was to investigate the role of parameter identification on the accuracy of results produced by finite element models accounting for bone-implant frictional contact, when the Penalty method is used. The sensitivity analysis of several numerical parameters that may govern the state of results was carried out. Two parameters, contact stiffness and convergence tolerance, were found to play a crucial role in establishing the accuracy of the finite element results. Based on the achieved results it was stated that any numerical-only study involving contact non-linearity and omitting careful qualification of the model limits should be rejected from any peer-reviewed journal.

Biomechanical Phenomena↗

PubMed1997

Linear least squares compartmental-model-independent parameter identification in PET.

A simplified approach involving linear-regression straight-line parameter fitting of dynamic scan data is developed for both specific and nonspecific models. Where compartmental-model topologies apply, the measured activity may be expressed in terms of: its integrals, plasma activity and plasma integrals--all in a linear expression with macroparameters as coefficients. Multiple linear regression, as in spreadsheet software, determines parameters for best data fits. Positron emission tomography (PET)-acquired gray-matter images in a dynamic scan are analyzed: both by this method and by traditional iterative nonlinear least squares. Both patient and simulated data were used. Regression and traditional methods are in expected agreement. Monte-Carlo simulations evaluate parameter standard deviations, due to data noise, and much smaller noise-induced biases. Unique straight-line graphical displays permit visualizing data influences on various macroparameters as changes in slopes. Advantages of regression fitting are: simplicity, speed, ease of implementation in spreadsheet software, avoiding risks of convergence failures or false solutions in iterative least squares, and providing various visualizations of the uptake process by straight line graphical displays. Multiparameter model-independent analyses on lesser understood systems is also made possible.

Algorithms↗

PubMed2004

On the structure and initial parameter identification of Gaussian RBF networks.

We consider the efficient initialization of structure and parameters of generalized Gaussian radial basis function (RBF) networks using fuzzy decision trees generated by fuzzy ID3 like induction algorithms. The initialization scheme is based on the proposed functional equivalence property of fuzzy decision trees and generalized Gaussian RBF networks. The resulting RBF network is compact, easy to induce, comprehensible, and has acceptable classification accuracy with stochastic gradient descent learning algorithm.

Algorithms↗