Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 21 for “"Linear Dynamical Systems"”.
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Linear dynamical systems with abstract state-spaces.
Thesis. 1978. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
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Learning Algorithms for Mixtures of Linear Dynamical Systems: A Practical Approach
… of an algorithm to learn a mixture of linear dynamical systems (LDS’s), and an analysis of algorithms to learn a single linear dynamical system. Following the work of Bakshi et al. ([1]), we implement a recent polynomial-time algorithm based on a tensor decomposition with learning …
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Simultaneous estimation of the state and noise statistics in linear dynamical systems.
Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics. Thesis. 1968. Sc.D.
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Parameter Identification and the Design of Experiments for Continuous Non-Linear Dynamical Systems
… analysis, control, and optimization of complex systems. One important step necessary to create an effective model is designing an experiment from which the unknown model parameter can be accurately identified and then verified. The strategy which one approaches this problem is dependent on the …
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Analysis of passive and active discrete-distributed linear dynamical systems using Green's function methods
… analysis of the dynamics of a large class of linear passive discrete-distributed mechanical systems has been performed, unifying and extending previous results in the field. A distributed parameter system, represented by the Green's function of its governing differential equation, and a …
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Linear system identification technique by time series analysis
… AT AUTHOR'S REQUEST.] Identification of linear dynamical systems using the finite difference time-domain (FDTD) method is inaccurate and problematic. This thesis investigates the use of time series analysis techniques for estimating parameters of a continuous-time (CT) model of linear …
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Generalized Volterra-Wiener and surrogate data methods for complex time series analysis
… describes the current state-of-the-art in nonlinear time series analysis, bringing together approaches from a broad range of disciplines including the non-linear dynamical systems, nonlinear modeling theory, time-series hypothesis testing, information theory, and self-similarity. We stress …
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Reinforcement Learning and Planning for Preference Balancing Tasks
Robots are often highly non-linear dynamical systems with many degrees of freedom, making solving motion problems computationally challenging. One solution has been reinforcement learning (RL), which learns through experimentation to automatically perform the near-optimal motions that complete a …
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Iterative Rational Krylov Algorithm for Unstable Dynamical Systems and Genaralized Coprime Factorizations
Generally, large-scale dynamical systems pose tremendous computational difficulties when applied in numerical simulations. In order to overcome these challenges we use several model reduction techniques. For stable linear models these techniques work very well and provide good approximations for …
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Measures from complexity science provide manufacturing companies with insights previously unavailable to them
… success in application of these tools. Complex systems SCience, with its origins in communications, control theory and non-linear dynamical systems, has provided previously unavailable insight into real-world systems. The applications to date in manufacturing have been in soft systems appearing …
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Gaussian processes for state space models and change point detection
… processes are capable of generalizing standard linear time series models. We cover two approaches: the Gaussian process time series model (GPTS) and the autoregressive Gaussian process (ARGP). We cover a variety of methods that greatly reduce the computational and memory complexity of Gaussian …
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Optimal sensor placement for linear systems
… of sensor placement is to observe the state of a dynamical system while using only a small part of the available output information. Thus, the observer does not need sensors at every possible node of the system. We use sensor placement because it is not practical for large-scale networks, such as …
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Geometric algorithms for input constrained systems with application to flight control.
… of analysis and control design for unstable linear dynamical systems having their input constrained by maximum amplitude and rate of the control signals. Although the results obtained are of a general nature, all the problems considered are induced by flight control applications. Moreover, …
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Dimension Reduction in Structured Dynamical Systems: Optimal-𝓗<sub>2</sub> Approximation, Data-Driven Balancing, and Real-Time Monitoring
… monitoring of large-scale and structured dynamical systems. In the first part, balancing-based methods for system-theoretic model reduction of linear time-invariant systems are considered. We generalize conditions for which the balanced truncation $mathcal{H}_{infty}$ error bound is known …
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Design principles for linear systems: Stability and optimality
… of them deals with problems in the design of linear time-invariant systems with certain prescribed properties, such as stability and cost optimality. The first part addresses theoretical questions arising in the design of autonomous decentralized systems. The network topology of such a system …
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Scalable Structure Learning of Graphical Models
… In this work, we focus on two popular models: dynamical linear systems and Markov random fields. For each case, we investigate major computational bottlenecks of baseline learning techniques. Next, we propose two frameworks that provide higher scalability using appropriate problem reformulation …
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Causal structure of networks of stochastic processes
… To capture the functional dependencies in a dynamical system, we introduce a new measure in this work and show that the FDGs are a generalization of DIGs. We also establish sufficient conditions under which the FDG defined by our measure is equivalent to the DIG. As an example, we study the …
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Dynamic non-linear system modelling using wavelet-based soft computing techniques
The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in …
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Efficient formulation and implementation of ensemble based methods in data assimilation
… to obtain improved estimates of the state of non-linear dynamical systems such as the atmosphere and/or the ocean. This work develops efficient ensemble-based methods for data assimilation. A major bottleneck in ensemble Kalman filter (EnKF) implementations is the solution of a linear system at …
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Analysis and synthesis techniques of nonlinear dynamical systems with applications to diagnostic of controlled thermonuclear fusion reactors
Nonlinear dynamical systems are of wide interest to engineers, physicists and mathematicians, and this is due to the fact that most of physical systems in nature are inherently non-linear. The nonlinearity of these systems has consequences on their time-evolution, which in some cases can be …
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