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: This paper proposes a three-stage procedure for parametric identification of piecewise affine autoregressive exogenous (PWARX) models and imposes that the identification error is bounded by a quantity /spl delta/.
Abstract: This paper proposes a three-stage procedure for parametric identification of piecewise affine autoregressive exogenous (PWARX) models. The first stage simultaneously classifies the data points and estimates the number of submodels and the corresponding parameters by solving the partition into a minimum number of feasible subsystems (MIN PFS) problem for a suitable set of linear complementary inequalities derived from data. Second, a refinement procedure reduces misclassifications and improves parameter estimates. The third stage determines a polyhedral partition of the regressor set via two-class or multiclass linear separation techniques. As a main feature, the algorithm imposes that the identification error is bounded by a quantity /spl delta/. Such a bound is a useful tuning parameter to trade off between quality of fit and model complexity. The performance of the proposed PWA system identification procedure is demonstrated via numerical examples and on experimental data from an electronic component placement process in a pick-and-place machine.
360 citations
TL;DR: A novel approach is adopted which employs a hybrid clustering and least squares algorithm which significantly enhances the real-time or adaptive capability of radial basis function models.
Abstract: Recursive identification of non-linear systems is investigated using radial basis function networks. A novel approach is adopted which employs a hybrid clustering and least squares algorithm. The recursive clustering algorithm adjusts the centres of the radial basis function network while the recursive least squares algorithm estimates the connection weights of the network. Because these two recursive learning rules are both linear, rapid convergence is guaranteed and this hybrid algorithm significantly enhances the real-time or adaptive capability of radial basis function models. The application to simulated real data are included to demonstrate the effectiveness of this hybrid approach.
359 citations
TL;DR: The system identification schemes using Laguerre models are extended and generalized to Kautz models, which correspond to representations using several different possible complex exponentials, and linear regression methods to estimate this sort of model from measured data are analyzed.
Abstract: In this paper, the problem of approximating a linear time-invariant stable system by a finite weighted sum of given exponentials is considered. System identification schemes using Laguerre models are extended and generalized to Kautz models, which correspond to representations using several different possible complex exponentials. In particular, linear regression methods to estimate this sort of model from measured data are analyzed. The advantages of the proposed approach are the simplicity of the resulting identification scheme and the capability of modeling resonant systems using few parameters. The subsequent analysis is based on the result that the corresponding linear regression normal equations have a block Toeplitz structure. Several results on transfer function estimation are extended to discrete Kautz models, for example, asymptotic frequency domain variance expressions. >
359 citations
01 Aug 1997
TL;DR: A number of recently developed concepts and techniques for BSI, which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input are reviewed.
Abstract: Blind system identification (BSI) is a fundamental signal processing technology aimed at retrieving a system's unknown information from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation, and blind image restoration. This paper reviews a number of recently developed concepts and techniques for BSI, which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input.
358 citations
Book•
30 Sep 2002
TL;DR: In this article, the authors present an identification model for small-scale rotorcraft, based on the Frequency Response System Identification (FRIS) model, which is used to identify small rotors.
Abstract: Foreword. Acknowledgements. Nomenclature. Acronyms. 1. Motivation and Background. 2. Frequency Response System Identification. 3. Development of the Identification Model. 4. Identification of the Model. 5. Characteristics of Small-Scale Rotorcraft. 6. Elements of Control Design. 7. Results, Milestones and Future Directions in Aerial Robotics. References. Index.
358 citations