5,680 research outputs found
Algorithms for deterministic balanced subspace identification
No full textNew algorithms for identification of a balanced state space representation are proposed. They are based on a procedure for the estimation of impulse response and sequential zero input responses directly from data. The proposed algorithms are more efficient than the existing alternatives that compute the whole Hankel matrix of Markov parameters. It is shown that the computations can be performed on Hankel matrices of the input–output data of various dimensions. By choosing wider matrices, we need persistency of excitation of smaller order. Moreover, this leads to computational savings and improved statistical accuracy when the data is noisy. Using a finite amount of input–output data, the existing algorithms compute finite time balanced representation and the identified models have a lower bound on the distance to an exact balanced representation. The proposed algorithm can approximate arbitrarily closely an exact balanced representation. Moreover, the finite time balancing parameter can be selected automatically by monitoring the decay of the impulse response. We show what is the optimal in terms of minimal identifiability condition partition of the data into “past” and “future”
Johannes C. de Moor, The Rise of Yahwism. The Roots of Israelite Monotheism, Peeters, Leuven, revised and enlarged edition 1997 (première édition 1990) (Bibliotheca Ephemeridum Theologicarum Lovaniensium 91)
No full textJoosten Jan. Johannes C. de Moor, The Rise of Yahwism. The Roots of Israelite Monotheism, Peeters, Leuven, revised and enlarged edition 1997 (première édition 1990) (Bibliotheca Ephemeridum Theologicarum Lovaniensium 91). In: Revue d'histoire et de philosophie religieuses, 79e année n°2, Avril-juin 1999. p. 247
Identification of nonlinear state-space systems using zero-input responses
No full textThis paper studies the generalization of linear subspace identification techniques to nonlinear systems. The basic idea is to combine nonlinear minimal realization techniques based on the Hankel operator with embedding theory used in time-series modeling. We show that under the assumption of zero-state observability, a collection of several zero-input responses can be used to construct a state sequence of the nonlinear system. This state sequence can then be used to estimate a state-space model via nonlinear regression. We also discuss how the zero-input responses can be obtained. The proposed method is illustrated using a pendulum as an example system.
Application of structured total least squares for system identification and model reduction
No full textThe following identification problem is considered: minimize the l2 norm of the difference between a given time series and an approximating one under the constraint that the approximating time series is a trajectory of a linear time invariant system of a fixed complexity. The complexity is measured by the input dimension and the maximum lag. The problem is known as the global total least squares and alternatively can be viewed as maximum likelihood identification in the errors-in-variables setup. Multiple time series and latent variables can be considered in the same setting. Special cases of the problem are autonomous system identification, noisy realization, and finite time optimal l2 model reduction. The identification problem is related to the structured total least squares problem. The paper presents an efficient software package that implements the theory in practice. The proposed method and software are tested on data sets from the database for the identification of systems DAISY
Análise de desempenho de estratégias no algoritmo de Progressive Hedging quando aplicado na solução do problema de planejamento da operação energética
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2013.O Planejamento da Operação Energética no Brasil é um problema de natureza estocástica, devido às incertezas relacionadas às variações climáticas, em virtude do uso da hidroeletricidade como principal fonte de energia do sistema elétrico brasileiro. Com o objetivo de representar adequadamente as incertezas envolvidas no problema, é importante resolver esse problema por meio de técnicas de Otimização Estocástica. O Setor Elétrico Brasileiro usa atualmente os algoritmos baseados na Decomposição de Benders para resolver o problema de Planejamento da Operação Energética. Entretanto, essa técnica não é o único meio existente de se resolver este problema. Outras técnicas de Programação Estocástica podem ser aplicadas, tais como o Progressive Hedging, objeto de estudo deste trabalho. O presente trabalho visa apresentar essa técnica aplicada ao problema de Planejamento da Operação Energética aplicado a Sistemas Hidrotérmicos, na sua forma mais usual e em modelagens que utilizam artifícios matemáticos, com o objetivo de proporcionar melhor desempenho computacional desta técnica de otimização ao problema de Planejamento da Operação Energética Operation Planning in Brazil is a problem of stochastic nature, due to uncertainties related to climate changes, due to the use of hydropower as the main energy source of the Brazilian electrical system. In order to represent the uncertainties involved in the problem adequately, it is important to solve this problem by Stochastic Optimization techniques. Currently, the Brazilian Electricity Sector uses algorithms based on Benders decomposition to solve the problem of Operation Planning. However, this technique is not the only way of solving this problem. Other Stochastic Programming techniques can be applied, such as the Progressive Hedging, focused in this work. This work aims to present this technique when applied to the problem of Operation Planning applied in Hydrothermal Systems, in its most usual shape and modeling using mathematical strategies, with the aim of providing better computational performance of this optimization technique to the problem of Operation Planning
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