Download or read book Estimation of Nonlinear Greybox Models for Marine Applications written by Fredrik Ljungberg and published by Linköping University Electronic Press. This book was released on 2020-05-27 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: As marine vessels are becoming increasingly autonomous, having accurate simulation models available is turning into an absolute necessity. This holds both for facilitation of development and for achieving satisfactory model-based control. When accurate ship models are sought, it is necessary to account for nonlinear hydrodynamic effects and to deal with environmental disturbances in a correct way. In this thesis, parameter estimators for nonlinear regression models where the regressors are second-order modulus functions are analyzed. This model class is referred to as second-order modulus models and is often used for greybox identification of marine vessels. The primary focus in the thesis is to find consistent estimators and for this an instrumental variable (IV) method is used. First, it is demonstrated that the accuracy of an IV estimator can be improved by conducting experiments where the input signal has a static offset of sufficient amplitude and the instruments are forced to have zero mean. This two-step procedure is shown to give consistent estimators for second-order modulus models in cases where an off-the-shelf applied IV method does not, in particular when measurement uncertainty is taken into account. Moreover, it is shown that the possibility of obtaining consistent parameter estimators for models of this type depends on how process disturbances enter the system and on the amount of prior knowledge about the disturbances’ probability distributions that is available. In cases where the first-order moments are known, the aforementioned approach gives consistent estimators even when disturbances enter the system before the nonlinearity. In order to obtain consistent estimators in cases where the first-order moments are unknown, a framework for estimating the first and second-order moments alongside the model parameters is suggested. The idea is to describe the environmental disturbances as stationary stochastic processes in an inertial frame and to utilize the fact that their effect on a vessel depends on the vessel’s attitude. It is consequently possible to infer information about the environmental disturbances by over time measuring the orientation of a vessel they are affecting. Furthermore, in cases where the process disturbances are of more general character it is shown that supplementary disturbance measurements can be used for achieving consistency. Different scenarios where consistency can be achieved for instrumental variable estimators of second-order modulus models are demonstrated, both in theory and by simulation examples. Finally, estimation results obtained using data from a full-scale marine vessel are presented. I takt med att marina farkoster blir mer autonoma ökar behovet av noggranna matematiska farkostmodeller. Modellerna behövs både för att förenkla utvecklingen av nya farkoster och för att kunna styra farkosterna autonomt med önskad precision. För att erhålla allmängiltiga modeller behöver olinjära hydrodynamiska effekter samt systemstörningar, främst orsakade av vind- och vattenströmmar, tas i beaktning. I det här arbetet undersöks metoder för att skatta okända storheter i modeller för marina farkoster givet observerad data. Undersökningen gäller en speciell typ av olinjära modeller som ofta används för att beskriva marina farkoster. Huvudfokus i arbetet är att erhålla konsistens, vilket betyder att de skattade storheterna ska anta rätt värden när mängden observerad data ökar. För det används en redan etablerad statistisk metod som baseras på instrumentvariabler. Det visas först att noggrannheten i modellskattningsmetoden kan förbättras om datainsamlingsexperimenten utförs på ett sätt så att farkosten har signifikant nollskild hastighet och instrumentvariablernas medelvärde dras bort. Den här tvåstegslösningen påvisas vara fördelaktig vid skattning av parametrar i den ovan nämnda modelltypen, framför allt då mätosäkerhet tas i beaktning. Vidare så visas det att möjligheten att erhålla konsistenta skattningsmetoder beror på hur mycket kännedom om systemstörningarna som finns tillgänglig på förhand. I fallet då de huvudsakliga hastigheterna på vind- och vattenströmmar är kända, räcker den tidigare nämnda tvåstegsmetoden bra. För att även kunna hantera det mer generella fallet föreslås en metod för att skatta de huvudsakliga hastigheterna och de okända modellparametrarna parallellt. Denna idé baserar sig på att beskriva störningarna som stationära i ett globalt koordinatsystem och att anta att deras effekt på en farkost beror på hur farkosten är orienterad. Genom att över tid mäta och samla in data som beskriver en farkosts kurs, kan man således dra slutsatser om de störningar som farkosten påverkas av. Utöver detta visas det att utnyttjande av vindmätningar kan ge konsistens i fallet med störningar av mer generell karaktär. Olika scenarion där konsistens kan uppnås visas både i teori och med simuleringsexempel. Slutligen visas också modellskattningsresultat som erhållits med data insamlad från ett fullskaligt fartyg.

Download or read book On Complexity Certification of Active Set QP Methods with Applications to Linear MPC written by Daniel Arnström and published by Linköping University Electronic Press. This book was released on 2021-03-03 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: In model predictive control (MPC) an optimization problem has to be solved at each time step, which in real-time applications makes it important to solve these efficiently and to have good upper bounds on worst-case solution time. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depends on parameters such as system states and reference signals. A popular class of methods for solving such QPs is active-set methods, where a sequence of linear systems of equations is solved. The primary contribution of this thesis is a method which determines which sequence of subproblems a popular class of such active-set algorithms need to solve, for every possible QP instance that might arise from a given linear MPC problem (i.e, for every possible state and reference signal). By knowing these sequences, worst-case bounds on how many iterations, floating-point operations and, ultimately, the maximum solution time, these active-set algorithms require to compute a solution can be determined, which is of importance when, e.g, linear MPC is used in safety-critical applications. After establishing this complexity certification method, its applicability is extended by showing how it can be used indirectly to certify the complexity of another, efficient, type of active-set QP algorithm which reformulates the QP as a nonnegative least-squares method. Finally, the proposed complexity certification method is extended further to situations when enhancements to the active-set algorithms are used, namely, when they are terminated early (to save computations) and when outer proximal-point iterations are performed (to improve numerical stability).

Download or read book Decentralized Estimation Using Conservative Information Extraction written by Robin Forsling and published by Linköping University Electronic Press. This book was released on 2020-12-17 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor networks consist of sensors (e.g., radar and cameras) and processing units (e.g., estimators), where in the former information extraction occurs and in the latter estimates are formed. In decentralized estimation information extracted by sensors has been pre-processed at an intermediate processing unit prior to arriving at an estimator. Pre-processing of information allows for the complexity of large systems and systems-of-systems to be significantly reduced, and also makes the sensor network robust and flexible. One of the main disadvantages of pre-processing information is that information becomes correlated. These correlations, if not handled carefully, potentially lead to underestimated uncertainties about the calculated estimates. In conservative estimation the unknown correlations are handled by ensuring that the uncertainty about an estimate is not underestimated. If this is ensured the estimate is said to be conservative. Neglecting correlations means information is double counted which in worst case implies diverging estimates with fatal consequences. While ensuring conservative estimates is the main goal, it is desirable for a conservative estimator, as for any estimator, to provide an error covariance which is as small as possible. Application areas where conservative estimation is relevant are setups where multiple agents cooperate to accomplish a common objective, e.g., target tracking, surveillance and air policing. The first part of this thesis deals with theoretical matters where the conservative linear unbiased estimation problem is formalized. This part proposes an extension of classical linear estimation theory to the conservative estimation problem. The conservative linear unbiased estimator (CLUE) is suggested as a robust and practical alternative for estimation problems where the correlations are unknown. Optimality criteria for the CLUE are provided and further investigated. It is shown that finding an optimal CLUE is more complicated than finding an optimal linear unbiased estimator in the classical version of the problem. To simplify the problem, a CLUE that is optimal under certain restrictions will also be investigated. The latter is named restricted best CLUE. An important result is a theorem that gives a closed form solution to a restricted best CLUE. Furthermore, several conservative estimation methods are described followed by an analysis of their properties. The methods are shown to be conservative and optimal under different assumptions about the underlying correlations. The second part of the thesis focuses on practical aspects of the conservative approach to decentralized estimation in configurations where the communication channel is constrained. The diagonal covariance approximation is proposed as a data reduction technique that complies with the communication constraints and if handled correctly can be shown to preserve conservative estimates. Several information selection methods are derived that can reduce the amount of data being transmitted in the communication channel. Using the information selection methods it is possible to decide what information other actors of the sensor network find useful.

Download or read book Control Models and Industrial Manipulators written by Erik Hedberg and published by Linköping University Electronic Press. This book was released on 2020-11-23 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two topics at the heart of this thesis are how to improve control of industrial manipulators and how to reason about the role of models in automatic control. On industrial manipulators, two case studies are presented. The first investigates estimation with inertial sensors, and the second compares control by feedback linearization to control based on gain-scheduling. The contributions on the second topic illustrate the close connection between control and estimation in different ways. A conceptual model of control is introduced, which can be used to emphasize the role of models as well as the human aspect of control engineering. Some observations are made regarding block-diagram reformulations that illustrate the relation between models, control and inversion. Finally, a suggestion for how the internal model principle, internal model control, disturbance observers and Youla-Kucera parametrization can be introduced in a unified way is presented.

Download or read book Uncertainties in Neural Networks written by Magnus Malmström and published by Linköping University Electronic Press. This book was released on 2021-04-06 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: In science, technology, and engineering, creating models of the environment to predict future events has always been a key component. The models could be everything from how the friction of a tire depends on the wheels slip to how a pathogen is spread throughout society. As more data becomes available, the use of data-driven black-box models becomes more attractive. In many areas they have shown promising results, but for them to be used widespread in safety-critical applications such as autonomous driving some notion of uncertainty in the prediction is required. An example of such a black-box model is neural networks (NNs). This thesis aims to increase the usefulness of NNs by presenting an method where uncertainty in the prediction is obtained by linearization of the model. In system identification and sensor fusion, under the condition that the model structure is identifiable, this is a commonly used approach to get uncertainty in the prediction from a nonlinear model. If the model structure is not identifiable, such as for NNs, the ambiguities that cause this have to be taken care of in order to make the approach applicable. This is handled in the first part of the thesis where NNs are analyzed from a system identification perspective, and sources of uncertainty are discussed. Another problem with data-driven black-box models is that it is difficult to know how flexible the model needs to be in order to correctly model the true system. One solution to this problem is to use a model that is more flexible than necessary to make sure that the model is flexible enough. But how would that extra flexibility affect the uncertainty in the prediction? This is handled in the later part of the thesis where it is shown that the uncertainty in the prediction is bounded from below by the uncertainty in the prediction of the model with lowest flexibility required for representing true system accurately. In the literature, many other approaches to handle the uncertainty in predictions by NNs have been suggested, of which some are summarized in this work. Furthermore, a simulation and an experimental studies inspired by autonomous driving are conducted. In the simulation study, different sources of uncertainty are investigated, as well as how large the uncertainty in the predictions by NNs are in areas without training data. In the experimental study, the uncertainty in predictions done by different models are investigated. The results show that, compared to existing methods, the linearization method produces similar results for the uncertainty in predictions by NNs. An introduction video is available at https://youtu.be/O4ZcUTGXFN0 Inom forskning och utveckling har det har alltid varit centralt att skapa modeller av verkligheten. Dessa modeller har bland annat använts till att förutspå framtida händelser eller för att styra ett system till att bete sig som man önskar. Modellerna kan beskriva allt från hur friktionen hos ett bildäck påverkas av hur mycket hjulen glider till hur ett virus kan sprida sig i ett samhälle. I takt med att mer och mer data blir tillgänglig ökar potentialen för datadrivna black-box modeller. Dessa modeller är universella approximationer vilka ska kunna representera vilken godtycklig funktion som helst. Användningen av dessa modeller har haft stor framgång inom många områden men för att verkligen kunna etablera sig inom säkerhetskritiska områden såsom självkörande farkoster behövs en förståelse för osäkerhet i prediktionen från modellen. Neuronnät är ett exempel på en sådan black-box modell. I denna avhandling kommer olika sätt att tillförskaffa sig kunskap om osäkerhet i prediktionen av neuronnät undersökas. En metod som bygger på linjärisering av modellen för att tillförskaffa sig osäkerhet i prediktionen av neuronnätet kommer att presenteras. Denna metod är välbeprövad inom systemidentifiering och sensorfusion under antagandet att modellen är identifierbar. För modeller såsom neuronnät, vilka inte är identifierbara behövs det att det tas hänsyn till tvetydigheterna i modellen. En annan utmaning med datadrivna black-box modeller, är att veta om den valda modellmängden är tillräckligt generell för att kunna modellera det sanna systemet. En lösning på detta problem är att använda modeller som har mer flexibilitet än vad som behövs, det vill säga en överparameteriserad modell. Men hur påverkas osäkerheten i prediktionen av detta? Detta är något som undersöks i denna avhandling, vilken visar att osäkerheten i den överparameteriserad modellen kommer att vara begränsad underifrån av modellen med minst flexibilitet som ändå är tillräckligt generell för att modellera det sanna systemet. Som avslutning kommer dessa resultat att demonstreras i både en simuleringsstudie och en experimentstudie inspirerad av självkörande farkoster. Fokuset i simuleringsstudien är hur osäkerheten hos modellen är i områden med och utan tillgång till träningsdata medan experimentstudien fokuserar på jämförelsen mellan osäkerheten i olika typer av modeller.Resultaten från dessa studier visar att metoden som bygger på linjärisering ger liknande resultat för skattningen av osäkerheten i prediktionen av neuronnät, jämfört med existerande metoder.

Download or read book System Identification With Matlab written by A. Smith and published by Createspace Independent Publishing Platform. This book was released on 2017-11-19 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops the work with Nonlinear Models and Time Series Identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. MATLAB System Identification Toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting.. It is possible to analyze time series data by identifying linear and nonlinear models, including AR, ARMA, and state-space models; forecast values The most important content that this book provides are the following: - When to Fit Nonlinear Models - Nonlinear Model Estimation - Nonlinear Model Structures - Nonlinear ARX Models - Hammerstein-Wiener Models - Nonlinear Grey-Box Models - Preparing Data for Nonlinear Identification - Identifying Nonlinear ARX Models - Prepare Data for Identification - Configure Nonlinear ARX Model Structure - Specify Estimation Options for Nonlinear ARX Models - Initialize Nonlinear ARX Estimation Using Linear Model - Estimate Nonlinear ARX Models in the App - Estimate Nonlinear ARX Models at the Command Line - Estimate Nonlinear ARX Models Initialized Using Linear ARX Models - Validate Nonlinear ARX Models - Using Nonlinear ARX Models - Linear Approximation of Nonlinear Black-Box Models - Nonlinear Black-Box Model Identification - Identifying Hammerstein-Wiener Models - Available Nonlinearity Estimators for Hammerstein-Wiener Models - Estimate Hammerstein-Wiener Models in the App . - Estimate Hammerstein-Wiener Models at the Command Line - Validating Hammerstein-Wiener Models - How the Software Computes Hammerstein-Wiener Model Output - Evaluating Nonlinearities (SISO) - Evaluating Nonlinearities (MIMO) - Simulation of Hammerstein-Wiener Model - Estimate Hammerstein-Wiener Models Initialized Using Linear OE Models - Estimate Linear Grey-Box Models - Estimate Continuous-Time Grey-Box Model for Heat Diffusion - Estimate Discrete-Time Grey-Box Model with Parameterized Disturbance - Estimate Coefficients of ODEs to Fit Given Solution - Estimate Model Using Zero/Pole/Gain Parameters - Estimate Nonlinear Grey-Box Models - Identifying State-Space Models with Separate Process and Measurement Noise Descriptions - Time Series Identification - Preparing Time-Series Data - Estimate Time-Series Power Spectra - Estimate AR and ARMA Models - Definition of AR and ARMA Models - Estimating Polynomial Time-Series Models in the App - Estimating AR and ARMA Models at the Command Line - Estimate State-Space Time Series Models - Identify Time-Series Models at the Command Line - Estimate ARIMA Models - Analyze Time-Series Models - Introduction to Forecasting of Dynamic System Response - Forecasting Time Series Using Linear Models - Forecasting Response of Linear Models with Exogenous Inputs - Forecasting Response of Nonlinear Models - Forecast the Output of a Dynamic System - Forecast Time Series Data Using an ARMA Model - Recursive Model Identification

Download or read book Sustainable Development and Innovations in Marine Technologies written by Petar Georgiev and published by CRC Press. This book was released on 2019-08-22 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sustainable Development and Innovations in Marine Technologies includes the papers presented at the 18th International Congress of the Maritime Association of the Mediterranean (IMAM 2019, Varna, Bulgaria, 9-11 September 2019). Sustainable Development and Innovations in Marine Technologies includes a wide range of topics: Aquaculture & Fishing; Construction; Defence & Security; Design; Dynamic response of structures; Degradation/ Defects in structures; Electrical equipment of ships; Human factors; Hydrodynamics; Legal/Social aspects; Logistics; Machinery & Control; Marine environmental protection; Materials; Navigation; Noise; Non-linear motions – manoeuvrability; Off-shore and coastal development; Off-shore renewable energy; Port operations; Prime movers; Propulsion; Safety at sea; Safety of Marine Systems; Sea waves; Seakeeping; Shaft & propellers; Ship resistance; Shipyards; Small & pleasure crafts; Stability; Static response of structures; Structures, and Wind loads. The IMAM series of Conferences started in 1978 when the first Congress was organised in Istanbul, Turkey. IMAM 2019 is the eighteenth edition, and in its nearly forty years of history, this biannual event has been organised throughout Europe. Sustainable Development and Innovations in Marine Technologies is essential reading for academics, engineers and all professionals involved in the area of sustainable and innovative marine technologies.

Download or read book Transportation Electrification written by Ahmed A. Mohamed and published by John Wiley & Sons. This book was released on 2023-01-05 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation Electrification Dive deep into the latest breakthroughs in electrified modes of transport In Transportation Electrification, an accomplished team of researchers and industry experts delivers a unique synthesis of detailed analyses of recent breakthroughs in several modes of electric transportation and a holistic overview of how those advances can or cannot be applied to other modes of transportation. The editors include resources that examine electric aircraft, rolling stock, watercraft, and vehicle transportation types and comparatively determine their stages of development, distinctive and common barriers to advancement, challenges, gaps in technology, and possible solutions to developmental problems. This book offers readers a breadth of foundational knowledge combined with a deep understanding of the issues afflicting each mode of transportation. It acts as a roadmap and policy framework for transportation companies to guide the electrification of transportation vessels. Readers will benefit from an overview of key standards and regulations in the electrified transportation industry, as well as: A thorough introduction to the various modes of electric transportation, including recent advances in each mode, and the technological and policy challenges posed by them An exploration of different vehicle systems, including recent advanced in hybrid and EV powertrain architectures and advanced energy management strategies Discussions of electrified aircraft, including advanced technologies and architecture optimizations for cargo air vehicle, passenger air vehicles, and heavy lift vertical take-off and landing craft In-depth examinations of rolling stock and watercraft-type vehicles, and special vehicles, including various system architectures and energy storage systems relevant to each Perfect for practicing professionals in the electric transport industry, Transportation Electrification is also a must-read resource for standardization body members, regulators, officials, policy makers, and undergraduate students in electrical and electronics engineering.

Download or read book System Identification with MATLAB Non Linear Models Odes and Time Series written by Marvin L. and published by Createspace Independent Publishing Platform. This book was released on 2016-10-23 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. Thisb book develops de next task with models: Nonlinear Black-Box Model Identification Nonlinear Model Identification Fit Nonlinear Models Identifying Nonlinear ARX Models Nonlinearity Estimators for Nonlinear ARX Models Estimate Nonlinear ARX Models in the GUI Estimate Nonlinear ARX Models at the Command Line Validating Nonlinear ARX Models Identifying Hammerstein-Wiener Models Nonlinearity Estimators for Hammerstein-Wiener Models Estimation Algorithm for Hammerstein-Wiener Models Validating Hammerstein-Wiener Models Linear Approximation of Nonlinear Black-Box Models ODE Parameter Estimation (Grey-Box Modeling) Estimating Linear Grey-Box Models Estimating Nonlinear Grey-Box Models After Estimating Grey-Box Models Estimating Coefficients of ODEs to Fit Given Solution Estimate Model Using Zero/Pole/Gain Parameters Time Series Identification Estimating Time-Series Power Spectra Estimate Time-Series Power Spectra Using the GUI Estimate Time-Series Power Spectra at the Command Line Estimating AR and ARMA Models Estimating Polynomial Time-Series Models in the GUI Estimating AR and ARMA Models at the Command Line Estimating State-Space Time-Series Models Estimating State-Space Models at the Command Line Identify Time-Series Models at Command Line Estimating Nonlinear Models for Time-Series Data Estimating ARIMA Models Analyzing of Time-Series Models Recursive Model Identification General Form of Recursive Estimation Algorithm Kalman Filter Algorithm Recursive Estimation and Data Segmentation Techniques in System Identification Toolbox Model Analysis Validating Models After Estimation Plotting Models in the GUI Simulating and Predicting Model Output Simulation and Prediction in the GUI Simulation and Prediction at the Command Line Predict Using Time-Series Model Residual Analysis Impulse and Step Response Plots Frequency Response Plots Displaying the Confidence Interval Noise Spectrum Plots Pole and Zero Plots Analyzing MIMO Models Akaike's Criteria for Model Validation Troubleshooting Models Unstable Models Missing Input Variables Complicated Nonlinearities Spectrum Estimation Using Complex Data System Identification Toolbox Blocks Using System Identification Toolbox Blocks in Simulink Models Identifying Linear Models Simulating Identified Model Output in Simulink Simulate Identified Model Using Simulink Software System Identification Tool GUI

Download or read book Nonlinear Tools for a Nonlinear World written by Hao Ye and published by . This book was released on 2015 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundamental objective in the study of dynamic systems is to understand and predict their behavior. The research presented in this thesis addresses this goal using the general framework of empirical dynamic modeling (EDM). In the classical approach, system behavior is described using fixed mathematical equations, and multiple effects are often treated as linearly separable (i.e. in a reductionist framework). In contrast, EDM applies Takens' Theorem and the method of time delay embeddings to reconstruct system dynamics from time series data. This gives EDM the flexibility to model nonlinear, state-dependent interactions that are otherwise challenging for traditionally linear mathematical models. The first part of this thesis applies EDM towards the study of sockeye salmon populations from the Fraser River in British Columbia, Canada in order to understand the factors that affect recruitment and to produce better models for the annual returns. Whereas classical (linear) fisheries models do not improve when incorporating the environment, I show that Fraser River sockeye salmon actually exhibit nonlinear dynamics, and therefore are not amenable to these methods. Instead, EDM models that can account for nonlinearity show improved forecasts, and moreover, benefit greatly from the incorporation of state-dependent environmental effects. In addition, I demonstrate that the abrupt changes in the salmon populations, correlated with North Pacific climate indices can be explained as state-dependent nonlinear behavior. Whereas classical fisheries models or linear correlations would suggest sudden shifts in behavior associated with climate regimes, an appropriate nonlinear lens indicates that environmental effects are state-dependent, and that aggregation of data at the regional level produces the apparent linear patterns. The second part of this thesis involves the development of new methods in the EDM framework to distill data (i.e. time series) into information (i.e. inferences and conclusions). I show that a lagged form of convergent cross mapping (CCM), a method to infer causation in time series, can greatly enhance its capabilities, by quantifying the time delay associated with causation. This new method can be used to distinguish between direct and indirect, transitive, effects as well as produce more reliable estimates of interaction strength. I also develop Multiview Embedding (MVE) to address the issues of noise and short time series length in high-dimensional complex systems. By using a multimodel approach that leverages the "equation-free'" framework of EDM, MVE combines multiple reconstructions of system behavior, producing more accurate and precise forecasts, and demonstrating that complexity can be an asset, because of how information about the system dynamics is duplicated across interacting variables. Finally, these methods are included in a software package for EDM, developed for the R statistical language. A user guide for this software package, including installation instructions and examples, is included as an appendix.

Download or read book Estimation of Nonlinear Models with Measurement Error written by Susanne Maria Schennach and published by . This book was released on 2000 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation of Nonlinear Models with Measurement Error Using Marginal Information written by Yingyao Hu and published by . This book was released on 2003 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Progress in Air Pollution Research written by Sergio P. Balduino and published by . This book was released on 2007 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pollution is an undesirable state of the natural environment being contaminated with harmful substances as a consequence of human activities so that the environment becomes harmful or unfit for living things; especially applicable to the contamination of soil, water, or the atmosphere by the discharge of harmful substances. In addition to the harm, either present or future and known or unknown, to living beings, pollution cleanup and surveillance are enormous financial drains of the economies of the world. This book presents the latest research in this growing field.

Download or read book Prediction Methods for Blood Glucose Concentration written by Harald Kirchsteiger and published by Springer. This book was released on 2015-11-24 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book tackles the problem of overshoot and undershoot in blood glucose levels caused by delay in the effects of carbohydrate consumption and insulin administration. The ideas presented here will be very important in maintaining the welfare of insulin-dependent diabetics and avoiding the damaging effects of unpredicted swings in blood glucose – accurate prediction enables the implementation of counter-measures. The glucose prediction algorithms described are also a key and critical ingredient of automated insulin delivery systems, the so-called “artificial pancreas”. The authors address the topic of blood-glucose prediction from medical, scientific and technological points of view. Simulation studies are utilized for complementary analysis but the primary focus of this book is on real applications, using clinical data from diabetic subjects. The text details the current state of the art by surveying prediction algorithms, and then moves beyond it with the most recent advances in data-based modeling of glucose metabolism. The topic of performance evaluation is discussed and the relationship of clinical and technological needs and goals examined with regard to their implications for medical devices employing prediction algorithms. Practical and theoretical questions associated with such devices and their solutions are highlighted. This book shows researchers interested in biomedical device technology and control researchers working with predictive algorithms how incorporation of predictive algorithms into the next generation of portable glucose measurement can make treatment of diabetes safer and more efficient.

Download or read book Current Index to Statistics Applications Methods and Theory written by and published by . This book was released on 1995 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.

Download or read book Uncertainty in parameter estimation for nonlinear dynamical models written by Christoph Droste and published by . This book was released on 1997 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Trends in Renewable Energies Offshore written by C. Guedes Soares and published by CRC Press. This book was released on 2022-11-02 with total page 1920 pages. Available in PDF, EPUB and Kindle. Book excerpt: Renewable energy resources offshore are a growing contributor to the total energy production in a growing number of countries. As a result the interest in the topic is increasing. Trends in Renewable Energies Offshore includes the papers presented at the 5th International Conference on Renewable Energies Offshore (RENEW 2022, Lisbon, Portugal, 8-10 November 2022), and covers recent developments and experiences gained in concept development, design and operation of such devices. The scope of the contributions is broad, covering all aspects of renewable energies offshore activities, including: • Resource assessment • Tidal Energy • Wave Energy • Wind Energy • Solar Energy • Renewable Energy Devices • Multiuse Platforms • Maintenance planning • Materials and structural design Trends in Renewable Energies Offshore will be of interest to academics and professionals involved or interested in applications of renewable energy resources offshore. The ‘Proceedings in Marine Technology and Ocean Engineering’ series is dedicated to the publication of proceedings of peer-reviewed international conferences dealing with various aspects of ‘Marine Technology and Ocean Engineering’. The Series includes the proceedings of the following conferences: the International Maritime Association of the Mediterranean (IMAM) conferences, the Marine Structures (MARSTRUCT) conferences, the Renewable Energies Offshore (RENEW) conferences and the Maritime Technology (MARTECH) conferences. The ‘Marine Technology and Ocean Engineering’ series is also open to new conferences that cover topics on the sustainable exploration and exploitation of marine resources in various fields, such as maritime transport and ports, usage of the ocean including coastal areas, nautical activities, the exploration and exploitation of mineral resources, the protection of the marine environment and its resources, and risk analysis, safety and reliability. The aim of the series is to stimulate advanced education and training through the wide dissemination of the results of scientific research.