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- Instituto Politécnico de Bragança
- Biblioteca Digitais de Teses e Dissertações da USP
- Encontro de Tecnologia Acustica Submarina
- Universidade do Algarve
- IEEE Xplore
- Brazilian Society of Chemical Engineering
- ABM, ABC, ABPol
- Universidade de Adelaide
- Kluwer Academic Publ
- Monash University
- Quens University
- University of Cambridge; Department of Pure Mathematics and Mathematical Statistics; Statistical Laboratory; Darwin College
- Oxford University Press
- Sociedade Brasileira de Engenharia Biomédica
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## Recursive parameter estimation algorithms

Fonte: Instituto Politécnico de Bragança
Publicador: Instituto Politécnico de Bragança

Tipo: Conferência ou Objeto de Conferência

Português

Relevância na Pesquisa

66.06%

#Adaptive control#Model based control#Parameter estimation#Recursive algorithms#Recursive least squares

Main adaptive control design approaches assume that a suitable dynamic model of the controlled process can be computed. In this way, recursive parameter estimation algorithms play can important role in tracking the time variant parameters of the process dynamic model. Thois paper describes the major algorithms used to compute the ttransfer function parameters of time varying ssssystems. The advantages and limitations of these techniques are illustrated by computing the parameters of a time varying discrete system, with known structure, under the presence of persistent and non-persistent information.

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## Metodologia de estimação de parâmetros de sistemas dinâmicos não-lineares com aplicação em geradores síncronos; Parameter estimation methodology of dynamical nonlinear systems with application in synchronous generators

Fonte: Biblioteca Digitais de Teses e Dissertações da USP
Publicador: Biblioteca Digitais de Teses e Dissertações da USP

Tipo: Tese de Doutorado
Formato: application/pdf

Publicado em 27/03/2009
Português

Relevância na Pesquisa

66.22%

#Estimação de parâmetros#Gerador síncrono#Nonlinear systems#Parameter estimation#Sensibilidade de trajetória#Sincronização#Sistemas não-lineares#Synchronization#Synchronous generator#Trajectory sensitivity

Este trabalho apresenta uma nova metodologia para estimar parâmetros de geradores síncronos baseada na análise de sensibilidade de trajetória. Esta nova metodologia foi concebida com o objetivo de suplantar dificuldades de convergência que a metodologia de sensibilidade de trajetória tradicional apresenta devido a: (i) baixa robustez com relação aos valores iniciais dos parâmetros e ruído nas medidas, (ii) impossibilidade de lidar com singularidades que podem se apresentar nas equações algébricas do modelo de EAD (equações algébrica-diferenciais) que levam a inexistência de soluções, especialmente quando os parâmetros estão distantes dos valores verdadeiros. Apesar de ter sido desenvolvida para resolver o problema de estimação de parâmetros do gerador síncrono, a metodologia é geral e pode ser aplicada para uma classe grande de sistemas dinâmicos não-lineares. Neste sentido, a principal contribuição desta tese é a proposição de uma nova metodologia baseada na sensibilidade de trajetória para estimar parâmetros de sistemas dinâmicos não-lineares restritos, ou seja, modelados por EADs. Mais precisamente, relaxa-se a restrição de igualdade do sistema dinâmico, substituindo-a por uma formulação alternativa baseada na minimização da função algébrica do modelo de EAD. Uma segunda contribuição desta tese está relacionada à modelagem do gerador. Neste sentido...

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## Experimental results of geometric and geoacosutic parameter estimation using a vector sensor array

Fonte: Encontro de Tecnologia Acustica Submarina
Publicador: Encontro de Tecnologia Acustica Submarina

Tipo: Conferência ou Objeto de Conferência

Publicado em /11/2010
Português

Relevância na Pesquisa

66.11%

The objective of this paper is to present an overview of the work developed at SiPLAB,
University of Algarve, with vector sensor data collected during Makai experiment 2005, in geometric and geoacoustic parameter estimation. During this experiment devoted to high frequency initiative, acoustic data were acquired by a four element vertical vector sensor array (VSA). A vector sensor is a directional sensor constituted by one omni directional pressure sensor and three velocity-meters, where both the acoustic pressure and the three particle velocity components are measured. The spatial filtering capabilities of the vector
sensors are used to estimate the direction of arrival (DOA) of low and high frequency
acoustic sources considering a single and a multiple sensor VSA. An inversion method
based on Bartlett estimator is used for three dimensional localization of ship’s noise where the noise source is estimated in range and depth taking into accounts the azimuth given by DOA. Moreover, this method is applied to seabed parameters estimation like sediment compressional speed, density and compressional attenuation, contributing to improve the resolution of these parameters.

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## Comparing the resolution of Bartlett and MVDR estimators for bottom parameter estimation using pressure and vector sensor short array data

Fonte: Universidade do Algarve
Publicador: Universidade do Algarve

Tipo: Conferência ou Objeto de Conferência

Publicado em //2013
Português

Relevância na Pesquisa

66.22%

This work compares the resolution of a pressure and vector sensor based conventional Bartlett estimator, with their MVDR estimator counterparts, in the context of bottom characterization with a short vertical array. Santos et al. [1]demonstrated the gain of a vector sensor array (VSA) based linear estimator (Bartlett) for generic parameter estimation.
Moreover, it was shown that for bottom characterization the highest resolution of the estimates were achieved with the vertical particle velocity measurements alone. The present work highlights the gain in parameter resolution of a VSA based MVDR estimator. It is shown, that also for a MVDR estimator, the vector sensor array data improves the resolution of parameter estimation. But, it is also shown, through simulations, that for bottom parameter estimation, the pressure based MVDR estimator has higher resolution and sidelobe attenuation than the VSA based Bartlett estimator. These results were verified for experimental data acquired by a four element, 30 cm long vertical VSA in the 8–14 kHz band, during the Makai Experiment 2005 sea trial, off Kauai I., Hawaii (USA).

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## Comparing the resolution of Bartlett and MVDR processors for bottom parameter estimation using pressure and vector sensor short array data

Fonte: IEEE Xplore
Publicador: IEEE Xplore

Tipo: Conferência ou Objeto de Conferência

Publicado em //2013
Português

Relevância na Pesquisa

66.22%

This work compares the resolution of a pressure and vector sensor based conventional Bartlett estimator, with their MVDR estimator counterparts, in the context of bottom characterization with a short vertical array. Santos et al. [1] demonstrated the gain of a vector sensor array (VSA) based linear estimator (Bartlett) for generic parameter estimation.
Moreover, it was shown that for bottom characterization the highest resolution of the estimates were achieved with the vertical particle velocity measurements alone. The present work highlights the gain in parameter resolution of a VSA based MVDR estimator. It is shown, that also for a MVDR estimator, the vector sensor array data improves the resolution of parameter estimation. But, it is also shown, through simulations, that for bottom parameter estimation, the pressure based MVDR estimator has higher resolution and sidelobe attenuation than the VSA based Bartlett estimator. These results were verified for
experimental data acquired by a four element, 30 cm long vertical VSA in the 8–14 kHz band, during the Makai Experiment 2005
sea trial, off Kauai I., Hawaii (USA).

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## Taking Variable Correlation into Consideration during Parameter Estimation

Fonte: Brazilian Society of Chemical Engineering
Publicador: Brazilian Society of Chemical Engineering

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/03/1998
Português

Relevância na Pesquisa

66.22%

Variable correlations are usually neglected during parameter estimation. Very frequently these are gross assumptions and may potentially lead to inadequate interpretation of final estimation results. For this reason, variable correlation and model parameters are sometimes estimated simultaneously in certain parameter estimation procedures. It is shown, however, that usually taking variable correlation into consideration during parameter estimation may be inadequate and unnecessary, unless independent experimental analysis of measurement procedures is carried out.

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## Parameter estimation of thermodynamic models for high-pressure systems employing a stochastic method of global optimization

Fonte: Brazilian Society of Chemical Engineering
Publicador: Brazilian Society of Chemical Engineering

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/09/2000
Português

Relevância na Pesquisa

66.1%

This paper presents the utilization of a stochastic global optimization method for the problem of parameter estimation in thermodynamic models. The method is based on an adaptation of the simulated annealing method to continuous variables. The proposed approach is applied to a vapor-solid equilibrium with supercritical carbon dioxide as solvent. Numerical results indicate that the simulated annealing method calculated a set of parameter values associated with considerably smaller errors as compared with a traditional method of local optimization.

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## The performance of simulated annealing in parameter estimation for vapor-liquid equilibrium modeling

Fonte: Brazilian Society of Chemical Engineering
Publicador: Brazilian Society of Chemical Engineering

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/03/2007
Português

Relevância na Pesquisa

66.15%

#Vapor-liquid equilibrium#Simulated annealing#Nonlinear parameter estimation#Error-in-variable method#Global optimization

In this paper we report the application and evaluation of the simulated annealing (SA) optimization method in parameter estimation for vapor-liquid equilibrium (VLE) modeling. We tested this optimization method using the classical least squares and error-in-variable approaches. The reliability and efficiency of the data-fitting procedure are also considered using different values for algorithm parameters of the SA method. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. However, in difficult problems it still can converge to local optimums of the objective function.

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## A software for parameter estimation in dynamic models

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/12/2008
Português

Relevância na Pesquisa

66.15%

A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES) has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.

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## Optimal measurement locations for parameter estimation of non linear distributed parameter systems

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/12/2010
Português

Relevância na Pesquisa

66.15%

A sensor placement approach for the purpose of accurately estimating unknown parameters of a distributed parameter system is discussed. The idea is to convert the sensor location problem to a classical experimental design. The technique consists of analysing the extrema values of the sensitivity coefficients derived from the system and their corresponding spatial positions. This information is used to formulate an efficient computational optimum experiment design on discrete domains. The scheme studied is verified by a numerical example regarding the chemical reaction in a tubular reactor for two possible scenarios; stable and unstable operation conditions. The resulting approach is easy to implement and good estimates for the parameters of the system are obtained. This study shows that the measurement location plays an essential role in the parameter estimation procedure.

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## Sensitivity analysis and parameter estimation of heat transfer and material flow models in friction stir welding

Fonte: ABM, ABC, ABPol
Publicador: ABM, ABC, ABPol

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/04/2014
Português

Relevância na Pesquisa

66.15%

Although numerical models of heat transfer and material flow have contributed to understand the underlying mechanisms of friction stir welding (FSW), there are certain input model parameters that can not be easily determined. Thus, the model predictions do not always agree with experimental results. In this work, sensitivity analysis and parameter estimation were applied to test heat transfer and material flow models. A forward-difference approximation was used to compute the sensitivity of the solution with respect to the unknown model parameters. The Levenberg-Marquardt (LM) method was applied to solve the nonlinear parameter estimation problem. The numerical models were developed by the finite element method (FEM). The way in which the unknown model parameters independently affect the results and the importance of the location of reference points that take part in the objective function were determined.

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## Fundamental numerical schemes for parameter estimation in computer vision.

Fonte: Universidade de Adelaide
Publicador: Universidade de Adelaide

Tipo: Tese de Doutorado

Publicado em //2008
Português

Relevância na Pesquisa

66.19%

#parametric estimation#maximum likelihood linear dependencies#generalised inverse#problem conditioning#homography fitting#trifocal tensor estimation#Computer vision.#Computer vision -- Mathematical models.#Parameter estimation -- Data processing.#Tensor algebra.

An important research area in computer vision is parameter estimation. Given a mathematical model and a sample of image measurement data, key parameters are sought to encapsulate geometric properties of a relevant entity. An optimisation problem is often formulated in order to find these parameters. This thesis presents an elaboration of fundamental numerical algorithms for estimating parameters of multi-objective models of importance in computer vision applications. The work examines ways to solve unconstrained and constrained minimisation
problems from the view points of theory, computational methods, and numerical performance.
The research starts by considering a particular form of multi-equation constraint function that characterises a wide class of unconstrained optimisation tasks. Increasingly sophisticated cost functions are developed within a consistent framework, ultimately resulting in the creation of a new iterative estimation method. The scheme operates in a maximum likelihood setting and yields near-optimal estimate of the parameters. Salient features of themethod are that it has simple update rules and exhibits fast convergence. Then, to accommodate models with functional dependencies, two variant of this initial algorithm are proposed. These methods are improved again by reshaping the objective function in a way that presents the original estimation problem in a reduced form. This procedure leads to a novel algorithm with enhanced stability and convergence properties.
To extend the capacity of these schemes to deal with constrained optimisation problems...

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## A bilinear approach to the parameter estimation of a general heteroscedastic linear system, with application to conic fitting

Fonte: Kluwer Academic Publ
Publicador: Kluwer Academic Publ

Tipo: Artigo de Revista Científica

Publicado em //2007
Português

Relevância na Pesquisa

66.15%

#Parameter estimation#Heteroscedastic uncertainty#Bilinear approach#Low-rank matrix approximation#Least squares estimate#Mahalanobis distance#Conic fitting

In this paper, we employ low-rank matrix approximation to solve a general parameter estimation problem: where a non-linear system is linearized by treating the carrier terms as separate variables, thereby introducing heteroscedastic noise. We extend the bilinear approach to handle cases with heteroscedastic noise, in the framework of low-rank approximation. The ellipse fitting problem is investigated as a specific example of the general theory. Despite the impression given in the literature, the ellipse fitting problem is still unsolved when the data comes from a small section of the ellipse. Although there are already some good approaches to the problem of ellipse fitting, such as FNS and HEIV, convergence in these iterative approaches is not ensured, as pointed out in the literature. Another limitation of these approaches is that they cannot model the correlations among different rows of the “general measurement matrix”. Our method, of employing the bilinear approach to solve the general heteroscedastic parameter estimation problem, overcomes these limitations: it is convergent, at least to a local optimum, and can cope with a general heteroscedastic problem. Experiments show that the proposed bilinear approach performs better than other competing approaches: although it is still far short of a solution when the data comes from a very small arc of the ellipse.; Pei Chen and David Suter

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## A bilinear approach to the parameter estimation of a general heteroscedastic linear system with application to conic fitting

Fonte: Monash University
Publicador: Monash University

Tipo: Relatório

Publicado em //2006
Português

Relevância na Pesquisa

66.15%

#Parameter estimation#heteroscedastic uncertainty#bilinear approach#low-rank matrix approximation#least squares estimate#Mahalanobis distance#conicfitting

In this paper, we study the parameter estimation problem in a general heteroscedastic linear system, by putting the problem in the framework of the bilinear approach to low-rank matrix approximation. The ellipse fitting problem is studied as a specific example of the general theory. Despite the impression given in the literature, the ellipse fitting problem is still unsolved when the data comes from a small section of the ellipse. Although there are already some good approaches to the problem of conic fitting, such as FNS and HEIV, convergence in these iterative approaches is not ensured, as pointed out in the literature. Another limitation of these approaches is that they can’t model the correlations among different rows of the “general measurement matrix”. Our method, of employing the bilinear approach to solve the general heteroscedastic parameter estimation problem, overcomes these limitations: it is convergent and can cope with a general heteroscedastic problem. Experiments show that the proposed bilinear approach performs slightly better than other competing approaches; Pei Chen and David Suter

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## Parameter estimation in nonlinear continuous-time dynamic models with modelling errors and process disturbances

Fonte: Quens University
Publicador: Quens University

Tipo: Tese de Doutorado
Formato: 1585120 bytes; application/pdf

Português

Relevância na Pesquisa

66.2%

#Parameter estimation#Dynamic models#BSpline#Maximum likelihood#Differential equations#Stochastic differential equations#Nonlinear models

Model-based control and process optimization technologies are becoming more commonly used by chemical engineers. These algorithms rely on fundamental or empirical models that are frequently described by systems of differential equations with unknown parameters. It is, therefore, very important for modellers of chemical engineering processes to have access to reliable and efficient tools for parameter estimation in dynamic models. The purpose of this thesis is to develop an efficient and easy-to-use parameter estimation algorithm that can address difficulties that frequently arise when estimating parameters in nonlinear continuous-time dynamic models of industrial processes.
The proposed algorithm has desirable numerical stability properties that stem from using piece-wise polynomial discretization schemes to transform the model differential equations into a set of algebraic equations. Consequently, parameters can be estimated by solving a nonlinear programming problem without requiring repeated numerical integration of the differential equations.
Possible modelling discrepancies and process disturbances are accounted for in the proposed algorithm, and estimates of the process disturbance intensities can be obtained along with estimates of model parameters and states. Theoretical approximate confidence interval expressions for the parameters are developed.
Through a practical two-phase nylon reactor example...

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## Parameter Estimation Techniques for Nonlinear Dynamic Models with Limited Data, Process Disturbances and Modeling Errors

Fonte: Quens University
Publicador: Quens University

Tipo: Tese de Doutorado

Português

Relevância na Pesquisa

66.24%

#Maximum Likelihood#Parameter Estimation#Expectation Maximization#Chemical Engineering#Modeling Error#Stochastic Disturbances

In this thesis appropriate statistical methods to overcome two types of problems that occur during parameter estimation in chemical engineering systems are studied. The first problem is having too many parameters to estimate from limited available data, assuming that the model structure is correct, while the second problem involves estimating unmeasured disturbances, assuming that enough data are available for parameter estimation. In the first part of this thesis, a model is developed to predict rates of undesirable reactions during the finishing stage of nylon 66 production. This model has too many parameters to estimate (56 unknown parameters) and not having enough data to reliably estimating all of the parameters. Statistical techniques are used to determine that 43 of 56 parameters should be estimated. The proposed model matches the data well. In the second part of this thesis, techniques are proposed for estimating parameters in Stochastic Differential Equations (SDEs). SDEs are fundamental dynamic models that take into account process disturbances and model mismatch. Three new approximate maximum likelihood methods are developed for estimating parameters in SDE models. First, an Approximate Expectation Maximization (AEM) algorithm is developed for estimating model parameters and process disturbance intensities when measurement noise variance is known. Then...

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## A methodology for parameter estimation in seaweed productivity modelling

Fonte: Kluwer
Publicador: Kluwer

Tipo: Artigo de Revista Científica

Publicado em //1993
Português

Relevância na Pesquisa

66.15%

This paper presents a combined approach for parameter estimation in models of primary production.
The focus is on gross primary production and nutrient assimilation by seaweeds.
A database of productivity determinations, biomass and mortality measurements and nutrient uptake
rates obtained over one year for Gelidium sesquipedale in the Atlantic Ocean off Portugal has been used.
Annual productivity was estimated by harvesting methods, and empirical relationships using mortality/
wave energy and respiration rates have been derived to correct for losses and to convert the estimates
to gross production.
In situ determinations of productivity have been combined with data on the light climate (radiation
periods, intensity, mean turbidity) to give daily and annual productivity estimates. The theoretical nutrient
uptake calculated using a 'Redfield ratio' approach and determinations of in situ N and P consumption
by the algae during incubation periods have also been compared.
The results of the biomass difference and incubation approaches are discussed in order to assess the
utility of coefficients determined in situ for parameter estimation in seaweed production models.

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## Maximum likelihood parameter estimation in time series models using sequential Monte Carlo

Fonte: University of Cambridge; Department of Pure Mathematics and Mathematical Statistics; Statistical Laboratory; Darwin College
Publicador: University of Cambridge; Department of Pure Mathematics and Mathematical Statistics; Statistical Laboratory; Darwin College

Tipo: Thesis; doctoral; PhD

Português

Relevância na Pesquisa

66.29%

Time series models are used to characterise uncertainty in many real-world dynamical phenomena. A time series model typically contains a static variable, called parameter, which parametrizes the joint law of the random variables involved in the definition of the model. When a time series model is to be fitted to some sequentially observed data, it is essential to decide on the value of the parameter that describes the data best, a procedure generally called parameter estimation.
This thesis comprises novel contributions to the methodology on parameter estimation in time series models. Our primary interest is online estimation, although batch estimation is also considered. The developed methods are based on batch and online versions of expectation-maximisation (EM) and gradient ascent, two widely popular algorithms for maximum likelihood estimation (MLE). In the last two decades, the range of statistical models where parameter estimation can be performed has been significantly extended with the development of Monte Carlo methods. We provide contribution to the field in a similar manner, namely by combining EM and gradient ascent algorithms with sequential Monte Carlo (SMC) techniques. The time series models we investigate are widely used in statistical and engineering applications.
The original work of this thesis is organised in Chapters 4 to 7. Chapter 4 contains an online EM algorithm using SMC for MLE in changepoint models...

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## Parameter estimation of ordinary differential equations

Fonte: Oxford University Press
Publicador: Oxford University Press

Tipo: Artigo de Revista Científica

Português

Relevância na Pesquisa

66.14%

#Keywords: Constrained optimization#Data fitting#Gauss-Newton approximation#Ordinary differential equations#Orthogonal cyclic reduction#Parameter estimation#SQP methods

This paper addresses the development of a new algorithm for parameter estimation of ordinary differential equations. Here, we show that (1) the simultaneous approach combined with orthogonal cyclic reduction can be used to reduce the estimation problem to an optimization problem subject to a fixed number of equality constraints without the need for structural information to devise a stable embedding in the case of non-trivial dichotomy and (2) the Newton approximation of the Hessian information of the Lagrangian function of the estimation problem should be used in cases where hypothesized models are incorrect or only a limited amount of sample data is available. A new algorithm is proposed which includes the use of the sequential quadratic programming (SQP) Gauss-Newton approximation but also encompasses the SQP Newton approximation along with tests of when to use this approximation. This composite approach relaxes the restrictions on the SQP Gauss-Newton approximation that the hypothesized model should be correct and the sample data set large enough. This new algorithm has been tested on two standard problems.

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## Parameter estimation of an artificial respiratory system under mechanical ventilation following a noisy regime

Fonte: Sociedade Brasileira de Engenharia Biomédica
Publicador: Sociedade Brasileira de Engenharia Biomédica

Tipo: Artigo de Revista Científica
Formato: text/html

Publicado em 01/01/2015
Português

Relevância na Pesquisa

66.18%

#Noisy ventilation#Respiratory care#Mechanical ventilator#Parameter estimation#Compliance#Respiratory system model

Abstract Introduction: This work concerns the assessment of a novel system for mechanical ventilation and a parameter estimation method in a bench test. The tested system was based on a commercial mechanical ventilator and a personal computer. A computational routine was developed do drive the mechanical ventilator and a parameter estimation method was utilized to estimate positive end-expiratory pressure, resistance and compliance of the artificial respiratory system. Methods The computational routine was responsible for establishing connections between devices and controlling them. Parameters such as tidal volume, respiratory rate and others can be set for standard and noisy ventilation regimes. Ventilation tests were performed directly varying parameters in the system. Readings from a calibrated measuring device were the basis for analysis. Adopting a first-order linear model, the parameters could be estimated and the outcomes statistically analysed. Results Data acquisition was effective in terms of sample frequency and low noise content. After filtering, cycle detection and estimation took place. Statistics of median, mean and standard deviation were calculated, showing consistent matching with adjusted values. Changes in positive end-expiratory pressure statistically imply changes in compliance...

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