Data Driven Identification Of Networks Of Dynamic Systems PDF Download
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Author | : Michel Verhaegen |
Publisher | : Cambridge University Press |
Total Pages | : 287 |
Release | : 2022-05-12 |
Genre | : Technology & Engineering |
ISBN | : 1316515702 |
Download Data-Driven Identification of Networks of Dynamic Systems Book in PDF, ePub and Kindle
A comprehensive introduction to identifying network-connected systems, covering models and methods, and applications in adaptive optics.
Author | : Steven L. Brunton |
Publisher | : Cambridge University Press |
Total Pages | : 615 |
Release | : 2022-05-05 |
Genre | : Computers |
ISBN | : 1009098489 |
Download Data-Driven Science and Engineering Book in PDF, ePub and Kindle
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author | : Rolf Isermann |
Publisher | : Springer Science & Business Media |
Total Pages | : 705 |
Release | : 2010-11-22 |
Genre | : Technology & Engineering |
ISBN | : 3540788794 |
Download Identification of Dynamic Systems Book in PDF, ePub and Kindle
Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.
Author | : J. Nathan Kutz |
Publisher | : SIAM |
Total Pages | : 241 |
Release | : 2016-11-23 |
Genre | : Science |
ISBN | : 1611974496 |
Download Dynamic Mode Decomposition Book in PDF, ePub and Kindle
Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.
Author | : Dhruv Khandelwal |
Publisher | : Springer Nature |
Total Pages | : 250 |
Release | : 2022-02-03 |
Genre | : Technology & Engineering |
ISBN | : 3030903435 |
Download Automating Data-Driven Modelling of Dynamical Systems Book in PDF, ePub and Kindle
This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user’s perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.
Author | : Chengpu Yu |
Publisher | : Springer Nature |
Total Pages | : 273 |
Release | : 2021-11-22 |
Genre | : Technology & Engineering |
ISBN | : 9811675740 |
Download Blind Identification of Structured Dynamic Systems Book in PDF, ePub and Kindle
This book is intended for researchers active in the field of (blind) system identification and aims to provide new identification ideas/insights for dealing with challenging system identification problems. It presents a comprehensive overview of the state-of-the-art in the area, which would save a lot of time and avoid collecting the scattered information from research papers, reports and unpublished work. Besides, it is a self-contained book by including essential algebraic, system and optimization theories, which can help graduate students enter the amazing blind system identification world with less effort.
Author | : Thomas Frederick Mussmann |
Publisher | : |
Total Pages | : 44 |
Release | : 2021 |
Genre | : Dynamics |
ISBN | : |
Download Data Driven Learning of Dynamical Systems Using Neural Networks Book in PDF, ePub and Kindle
We review general numerical approaches for discovering governing equations through data driven equation recovery. That is when the equations governing a dynamical system is unknown and depends on some hidden subset of variables. We review the structure of Neural Networks, Residual Neural Networks, and Recurrent Neural Networks. We also discuss the Mori Zwanzig formulation using history to substitute for hidden variables. We explore two examples, first is modeling Neuron Bursting with hidden variables using a Neural Network. Second, we examine particle traffic models and select one, which we dimensionally reduce and then attempt to predict future state from this dimensional reduction.
Author | : Samuel Rudy |
Publisher | : |
Total Pages | : 164 |
Release | : 2019 |
Genre | : Differential equations, Partial |
ISBN | : |
Download Computational Methods for System Identification and Data-driven Forecasting Book in PDF, ePub and Kindle
This thesis develops several novel computational tools for system identification and data-driven forecasting. The material is divided into four chapters: data-driven identification of partial differential equations, neural network interpolation of velocity field data from trajectory measurements, smoothing of high dimensional nonlinear time series, and an application of data-driven forecasting in biology. We first develop a novel computational method for identifying partial differential equations (PDEs) from measurements in the spatio-temporal domain. Building on past methods in sparse regression, we formulate a regression problem to select the active terms of a PDE from a large library of candidate basis functions. In contrast to many data-driven forecasting methods, the proposed algorithm yields exact representations of the dynamics. This has the advantage of allowing for future state prediction from novel initial and boundary conditions as well as rigorous mathematical analysis. The method is also extended to the case where coefficients vary either in space or time. We demonstrate the ability to accurately learn the correct active terms and their magnitudes on a variety on canonical partial differential equations. We also develop a method for interpolating the velocity fields of smooth dynamical systems using neural networks. We specifically focus on addressing the issue of learning from noisy and limited data. We construct a cost function for training neural network interpolations of velocity fields from trajectory measurements that explicitly accounts for measurement noise. The need to numerically differentiate data is avoided by placing the neural network interpolation of velocity within an explicit timestepping scheme and training as a flow map rather than directly on the velocity field. The proposed framework is shown to be capable of learning accurate forecasting models even when data is corrupted by significant levels of noise. We also consider some limitations of using neural networks as forecasting models for dynamical systems. Using test problems with known dynamics, we show that neural networks are able to accurately interpolate a vector field only where data is collected and generally exhibit high generalization error. Some guidelines are proposed regarding the contexts in which neural networks may or may not be useful in practice. For datasets where dynamics are known either completely or up to a set of parameters, we develop a novel smoothing technique based on soft-adherence to governing equations. The proposed method may be applicable to smoothing data from deterministic dynamical systems where high dimensionality or nonlinearity make sequential Bayesian methods impractical. We test the method on several canonical problems from data assimilation and show that it is robust to exceptionally high levels of noise as well as noise with non-zero mean and temporally autocorrelated noise. The last section of this thesis develops a data-driven forecasting model for the half-sarcomere, a small component of skeletal muscle tissue. Current models of the half-sarcomere currently require computationally expensive Monte Carlo simulations to resolve the effects of filament compliance. We seek to replicate the dynamic behavior realized by Monte Carlo simulation of the half-sarcomere at a lower cost. Drawing inspiration from surrogate and reduced order modeling, we apply a course graining to the variables tracked by the Monte Carlo simulation and learn a dynamics model on the course grained variables using data. We find that the resulting data-driven model effectively reproduces force traces and dynamics of the course grained state when given novel input parameters. Taken together, the innovations presented in this thesis represent a modest contribution to the field of data-driven methods for system identification and forecasting. In the concluding chapter, we highlight several exciting directions that build upon and improve the research presented in this thesis.
Author | : Gianluigi Pillonetto |
Publisher | : Springer Nature |
Total Pages | : 394 |
Release | : 2022-05-13 |
Genre | : Computers |
ISBN | : 3030958604 |
Download Regularized System Identification Book in PDF, ePub and Kindle
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book.
Author | : Long Jin |
Publisher | : Frontiers Media SA |
Total Pages | : 301 |
Release | : 2024-07-24 |
Genre | : Science |
ISBN | : 2832552013 |
Download Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications Book in PDF, ePub and Kindle
Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.