Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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TL;DR: In this paper, the authors used the covariance driven stochastic subspace identification method (SSI-COV) and a hierarchical clustering algorithm for the identification of the bridge first 12 modes.
338 citations
TL;DR: This paper presents a new method (constrained ALS) to estimate the variances of the disturbances entering the process using routine operating data and guarantees positive semidefinite covariance estimates by adding constraints to the ALS problem.
337 citations
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TL;DR: This work bridges the gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.
Abstract: Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies. A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties. This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.
337 citations
TL;DR: A new algorithm for testing global identifiability of nonlinear dynamic models, based on differential algebra, is presented, capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters.
Abstract: A prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. Here, the authors present a new algorithm for testing global identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global identifiability of nonlinear models of biological and physiological systems are presented.
336 citations
TL;DR: A recently developed general theory for basis construction will be presented, that is a generalization of the classical Laguerre theory, particularly exploiting the property that basis function models are linearly parametrized.
336 citations