نتایج جستجو برای: state space and subspace identification
تعداد نتایج: 17066051 فیلتر نتایج به سال:
This paper presents a Basis Pursuit DeNoising (BPDN) sparse estimation approach as a regularization technique in a predictor-based subspace method for the identification of Linear ParameterVarying (LPV) state-space systems. It is known that in this identification method, the choice of the past window of a state predictor factorization will influence the conditioning of the main parameter estima...
In this paper we consider the reduced rank regression problem min rank ¯ L=n,L 3 det Y α − ¯ LP β − L 3 U α Y α − ¯ LP β − L 3 U α T solved by maximum-likelihood-inspired State-Space Subspace System Identification algorithms. We conclude that the determinant criterion is, due to potential rank-deficiencies, not general enough to handle all problem instances. The main part of the paper analyzes ...
in proposition 2.6 in (g. gruenhage, a. lutzer, baire and volterra spaces, textit{proc. amer. math. soc.} {128} (2000), no. 10, 3115--3124) a condition that every point of $d$ is $g_delta$ in $x$ was overlooked. so we proved some conditions by which a baire space is equivalent to a volterra space. in this note we show that if $x$ is a monotonically normal $t_1$...
attempts have been made to study the thermodynamic behavior of 1,3 butadiene purification columns with the aim of retrofitting those columns to more energy efficient separation schemes. 1,3 butadiene is purified in two columns in series through being separated from methyl acetylene and 1,2 butadiene in the first and second column respectively. comparisons have been made among different therm...
a major concern in the last few years has been the fact that the cultural centers are keeping distance with what they have been established for and instead of reproducing the hegemony, they have turned into a place for resistance and reproduction of resistance against hegemony. because the cultural centers, as urban public spaces in the last two decades, have been the subject of ideological dis...
This paper describes how the state space basis of models identi ed with subspace identi cation algorithms can be determined. It is shown that this basis is determined by the input spectrum and by user de ned input and output weightings. Through the connections between subspace identi cation and frequency weighted balancing, the state space basis of the subspace identi ed models is shown to coin...
The paper presentes a numerically stable and general algorithm for identification and realization of a complete dynamic linear state space model, including the system order, for combined deterministic and stochastic systems from time series. A special property of this algorithm is that the innovations covariance matrix and the Markov parameters for the stochastic sub-system are determined direc...
It has been experimentally verified that most commonly used subspace methods for identification of linear state-space systems with exogenous inputs may, in certain experimental conditions, run into ill-conditioning and lead to ambiguous results. An analysis of the critical situations has lead us to propose a new algorithmic structure which could be used either to test difficult cases and/or to ...
Ab~tract-This paper presents a method to determine a nonlinear state space model from a 6nite number of measurements of the inputs and outputs. The method is based on embedding theory for nonlinear systems, and can be viewed as an extension of the subspace identification method for linear systems. The paper describes the underlying theory and provides some guidelines for using the method in pra...
Chemical process variables are always driven by random noise and disturbances. The closed-loop control yields process measurements that are auto & cross correlated. The influence of auto & cross correlations on statistical process control (SPC) is investigated in detail. It is revealed both auto and cross correlations among the variables will cause unexpected false alarms. Dynamic PCA and ARMA-...
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