The purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In this simulation study, the Kalman filter technique is used to produce sets of predictions and to infer the stiffness parameter based on noisy measurement. From this, the performance of online parameter identification can be tested with respect to different noise levels. This research is based on simulation work showcasing how effective the Kalman filtering techniques are in dealing with analytical uncertainties of data. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Combined Cooling Heating and Power (CCHP) systems have been recognized as a viable alternative to conventional electrical and thermal energy generation in buildings because of their high efficiency, low environmental impact, and power grid independence. Many researchers have presented models for comparing CCHP systems to conventional systems and for optimizing CCHP systems. However, many of the errors and uncertainties that affect these modeling efforts have not been adequately addressed in the literature. This dissertation will focus on the following key issues related to errors and uncertainty in CCHP system modeling: (a) detailed uncertainty analysis of a CCHP system model with novel characterization of weather patterns, fuel prices and component efficiencies; (b) sensitivity analysis of a method for estimating the hourly energy demands of a building using Department of Energy (DOE) reference building models in combination with monthly utility bills; (c) development of a practical technique for selecting the optimal Power Generation Unit (PGU) for a given building that is robust with respect to fuel cost and weather uncertainty; (d) development of a systematic method for integrated calibration and parameter estimation of thermal system models. The results from the detailed uncertainty analysis show that CCHP operational strategies can effectively be assessed using steady state models with typical year weather data. The results of the sensitivity analysis reveal that the DOE reference buildings can be adjusted using monthly utility bills to represent the hourly energy demands of actual buildings. The optimal PGU sizing study illustrates that the PGU can be selected for a given building in consideration of weather and fuel cost uncertainty. The results of the integrated parameter estimation study reveal that using the integrated approach can reduce the effect of measurement error on the accuracy of predictive thermal system models.

All hydrological models incur various uncertainties that can be broadly classified into three categories: measurement, structural, and parametric uncertainties. Measurement uncertainty exists due to error in measurements of properties and variables (e.g. streamflows that are typically an output and rainfall that serves as an input to hydrological models). Structural uncertainty exists due errors in mathematical representation of real-world hydrological processes. Parametric uncertainty exists due to structural and measurement uncertainty and limited amount of data availability for calibration. <br>Several studies have addressed the problem of measurement and parametric uncertainties but studies on structural uncertainty are lacking. Specifically, there does not exist any model that can be used to quantify structural uncertainties at an ungauged location. This was the first objective of the study: to develop a model of structural uncertainty that can be used to quantify total uncertainty (including structural uncertainty) in streamflow estimates at ungauged locations in a watershed. The proposed model is based on the idea that since the effect of structural uncertainty is to introduce a bias into the parameter estimation, one way to accommodate structural uncertainty is to compensate for this bias. The developed model was applied to two watersheds: Upper Wabash Busseron Watershed (UWBW) and Lower Des Plaines Watershed (LDPW). For UWBW, mean daily streamflow data were used while for LDPW mean hourly streamflow data were used. The proposed model worked well for mean daily data but failed to capture the total uncertainties for hourly data likely due to higher measurement uncertainties in hourly streamflow data than what was assumed in the study.<br>Once a hydrological and error model is specified, the next step is to estimate model- and error- parameters. Parameter estimation in hydrological modeling may be carried out using either formal Bayesian methodology or informal Bayesian methodology. In formal Bayesian methodology, a likelihood function, motivated from probability theory, is specified over a space of models (or residuals), and a prior probability distribution is assigned over the space of models. There has been significant debate on whether the likelihood functions used in Bayesian theory are justified in hydrological modeling. However, relatively little attention has been given to justification of prior probabilities. In most hydrological modeling studies, a uniform prior over hydrological model parameters is used to reflect a complete lack of knowledge of a modeler about model parameters before calibration. Such a prior is also known as a non-informative prior. The second objective of this study was to scrutinize the assumption of uniform prior as non-informative using the principle of maximum information gain. This principle was used to derive non-informative priors for several hydrological models, and it was found that the obtained prior was significantly different from a uniform prior. Further, the posterior distributions obtained by using this prior were significantly different from those obtained by using uniform priors.<br>The information about uncertainty in a modeling exercise is typically obtained from residual time series (the difference between observed and simulated streamflows) which is an aggregate of structural and measurement uncertainties for a fixed model parameter set. Using this residual time series, an estimate of total uncertainty may be obtained but it is impossible to separate structural and measurement uncertainties. The separation of these two uncertainties is, however, required to facilitate the rejection of deficient model structures, and to identify whether the model structure or the measurements need to be improved to reduce the total uncertainty. The only way to achieve this goal is to obtain an estimate of measurement uncertainty before model calibration. An estimate of measurement uncertainties in streamflow can be obtained by using rating-curve analysis but it is difficult to obtain an estimate of measurement uncertainty in rainfall. In this study, the classic idea of repeated sampling is used to get an estimate of measurement uncertainty in rainfall and streamflows. In the repeated sampling scheme, an experiment is performed several times under identical conditions to get an estimate of measurement uncertainty. This kind of repeated sampling, however, is not strictly possible for environmental observations, therefore, repeated sampling was used in an approximate manner using a machine learning algorithm called random forest (RF). The main idea is to identify rainfall-runoff events across several different watersheds which are similar to each other such that they can be thought of as different realizations of the same experiment performed under identical conditions. The uncertainty bounds obtained by RF were compared against the uncertainty band obtained by rating-curve analysis and runoff-coefficient method. Overall, the results of this study are encouraging in using RF as a pseudo repeated sampler. <br>In the fourth objective, importance of uncertainty in estimated streamflows at ungauged locations and uncertainty in measured streamflows at gauged locations is illustrated in water quality modeling. The results of this study showed that it is not enough to obtain an uncertainty bound that envelops the true streamflows, but that the individual realizations obtained by the model of uncertainty should be able to emulate the shape of the true streamflow time series for water quality modeling.

The beta distribution is useful in modeling continuous random variables that lie between 0 and 1, such as proportions and percentages. The beta distribution takes on many different shapes and may be described by two shape parameters, alpha and beta, that can be difficult to estimate. Maximum likelihood and method of moments estimation are possible, though method of moments is much more straightforward. We examine both of these methods here, and compare them to three more proposed methods of parameter estimation: 1) a method used in the Program Evaluation and Review Technique (PERT), 2) a modification of the two-sided power distribution (TSP), and 3) a quantile estimator based on the first and third quartiles of the beta distribution. We find the quantile estimator performs as well as maximum likelihood and method of moments estimators for most beta distributions. The PERT and TSP estimators do well for a smaller subset of beta distributions, though they never outperform the maximum likelihood, method of moments, or quantile estimators. We apply these estimation techniques to two data sets to see how well they approximate real data from Major League Baseball (batting averages) and the U.S. Department of Energy (radiation exposure). We find the maximum likelihood, method of moments, and quantile estimators perform well with batting averages (sample size 160), and the method of moments and quantile estimators perform well with radiation exposure proportions (sample size 20). Maximum likelihood estimators would likely do fine with such a small sample size were it not for the iterative method needed to solve for alpha and beta, which is quite sensitive to starting values. The PERT and TSP estimators do more poorly in both situations. We conclude that in addition to maximum likelihood and method of moments estimation, our method of quantile estimation is efficient and accurate in estimating parameters of the beta distribution.

This thesis discusses topics relevant to indoor unmanned quadrotor navigation and control. These topics include: quadrotor modeling, sensor modeling, quadrotor parameter estimation, sensor calibration, quadrotor state estimation using onboard sensors, and cooperative GPS navigation. Modeling the quadrotor, sensor modeling, and parameter estimation are essential components for quadrotor navigation and control. This thesis investigates prior work and organizes a wide variety of models and calibration methods that enable indoor unmanned quadrotor flight. Quadrotor parameter estimation using a particle filter is a contribution that extends current research in the area. This contribution is novel in that it applies the particle filter specifically to quadrotor parameter estimation as opposed to quadrotor state estimation. The advantages and disadvantages of such an approach are explained. Quadrotor state estimation using onboard sensors and without the aid of GPS is also discussed, as well as quadrotor pose estimation using the Extended Kalman Filter with an inertial measurement unit and simulated 3D camera updates. This is done using two measurement updates: one from the inertial measurement unit and one from the simulated 3D camera. Finally, we demonstrate that when GPS lock cannot be obtained by an unmanned vehicle individually. A group of cooperative robots with pose estimates to one anther can exploit partial GPS information to improve global position estimates for individuals in the group. This method is advantageous for robots that need to navigate in environments where signals from GPS satellites are partially obscured or jammed.

The motivation for this research is the need to improve performance of the autonomous flight of a tailsitter UAV. Tailsitter aircraft combine the hovering and vertical take-off and landing capability of a rotorcraft with the long endurance flight capability of a fixed-wing aircraft. The particular aircraft used in this research is the V-Bat, a tailsitter UAV with a conventional wing and the propeller and control surfaces located within a ducted-fan tail assembly. This research focuses on identifying the models and parameters of the V-Bat in hover and level flight as a basis for the design of the control systems for hover, level, and transition modes of flight.Models and parameters were identified from experimental data. Wind-tunnel tests, bench tests, and flight tests were performed in a variety of flight conditions. Wind tunnel tests yielded force and moment coefficients over the full flight envelope of the V-Bat. Models and parameters for longitudinal, lateral, and hover flight are presented. Bench tests were conducted to enhance understanding about the ducted-fan propulsion system and the effectiveness of the control surfaces. The thrust characteristics of the ducted fan were measured. Control derivatives were derived from force and moment measurements. Flight tests were completed to obtain dynamic models of the V-Bat in hover flight. Using frequency-domain system identification methods, frequency-response and transfer function models of roll, pitch, and yaw responses to aileron, elevator, and rudder control input were derived.The results obtained from these experimental tests were used to identify models and parameters of the V-Bat aircraft, giving insight into its behavior and enhancing the control analysis and simulation capabilities for this aircraft, thus providing the increased levels of understanding needed for autonomous flight.

[pt] Este trabalho apresenta um dos procedimentos adotados na avaliação do desempenho da estimação de parâmetros. Este procedimento consiste na determinação de limites inferiores no erro médio quadrático da estimação. São examinados os limites de Cramér-Rao e Ziv-Zakai abordando-se especialmente sua aplicação ao problema da estimação de parâmetros de um processo aleatório gaussiano. Em exemplo ilustrativo os resultados obtidos são aplicados a estimação dos parâmetros da densidade espectral de potência de um processo, supondo-se para esta densidade, um modelo racional simples. / [en] This work presents one of the possible approaches of evaluating the parameter estimation performance. This approach is based on the determination of lover bounds for estimate mean square error. The Cramér-Rao and Ziv-Zakai bounds are studied mainly in the case of gaussian random process parameter estimation. The results are applied as an example to the estimation of the power spectral density parameters of a random process. A simple rational model is used to represent this spectral density.

This thesis focuses on an adaptive quadratic spiking model of a motoneuron that is both versatile in its ability to represent a range of experimentally observed neuronal firing patterns as well as computationally efficient for large network simulation. The objective of research is to fit membrane voltage data to the model using a parameter estimation approach involving simulated annealing. By manipulating the system dynamics of the model, a realizable model with linear parameterization (LP) can be obtained to simplify the estimation process. With a persistently excited current input applied to the model, simulated annealing is used to efficiently determine the best model parameters that minimize the square error function between the membrane voltage reference data and data generated by the LP model. Results obtained through simulation of this approach show feasibility to predict a range of different neuron firing patterns.

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