Journal Article10.1016/S0005-1098(01)00003-6
A frequency-domain iterative identification algorithm using general orthonormal basis functions
18
TL;DR: A two-step frequency-domain identification algorithm is proposed, in which the identified model is parameterized in terms of general orthonormal basis functions, which is applied to experimental frequency response data obtained from a compact disk player.
read more
About: This article is published in Automatica. The article was published on 01 May 2001. The article focuses on the topics: Orthonormal basis & Orthonormality.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Iterative Update of the Pole Locations in a Wiener-Schetzen Model
Koen Tiels,Joannes Schoukens +1 more
TL;DR: This paper proposes an iterative method to update the pole locations of the Wiener-Schetzen model without making repeated experiments, and analyzes the causes for the mismatch between the estimated and the true pole locations.
2
Modelagem de sistemas não-lineares por base de funções ortonormais generalizadas com funções internas
Jeremias Barbosa Machado,Wagner Caradori do Amaral +1 more
- 24 Feb 2011
TL;DR: In this article, a modelagem and identificacao of sistemas dinâmicos nao-lineares estaveis atraves of modelos fuzzy Takagi-Sugeno (TS) e/ou Volterra, ambos com estruturas formadas by bases of funcoes ortonormais generalizadas (GOBF) with funcoe internas.
2
A novel recursive empirical frequency-domain optimal parameter estimate method based on reduced-order matrix approximation
Ting Chen,Songlin Chen,Xin Huo,Xi Zhang +3 more
- 27 Jul 2016
TL;DR: The extensive simulations show that compared with the existing REFOP algorithm, the proposed one can perform faster and more precise parameters estimation whether the input signal is weak or not, which verifies its effectiveness and superiority sufficiently.
A Data-Driven Basis Function Approach in Nonparametric Nonlinear System Identification
Er-Wei Bai,Changming Cheng +1 more
- 01 Jan 2018
TL;DR: In this chapter, a data-driven orthogonal basis function approach is proposed for nonparametric FIR nonlinear system identification and approaches are proposed for model order determination and regressor selection along with their theoretical justifications.
References
Transfer function synthesis as a ratio of two complex polynomials
C. Sanathanan,J. Koerner +1 more
TL;DR: In this paper, the authors presented an analytic description of the complex transfer function superior to that given by minimization of the "weighted" sum of the squares of the errors in magnitude.
Control oriented system identification: a worst-case/deterministic approach in H/sub infinity /
TL;DR: The authors formulate and solve two related control-oriented system identification problems for stable linear shift-invariant distributed parameter plants, each involving identification of a point sample of the plant frequency response from a noisy, finite, output time series obtained in response to an applied sinusoidal input.
531
A unifying construction of orthonormal bases for system identification
Brett Ninness,Fredrik Gustafsson +1 more
TL;DR: This construction provides a unifying formulation of many previously studied orthonormal bases since the common FIR and recently popular Laguerre and two-parameter Kautz model structures are restrictive special cases of the construction presented here.
460
A generalized orthonormal basis for linear dynamical systems
TL;DR: It is shown how to exploit these generalized basis functions to increase the speed of convergence in a series expansion, i.e., to obtain a good approximation by retaining only a finite number of expansion coefficients.