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Iterative learning identification: Dynamic parametrization modeling and comparison
International Journal of Robust and Nonlinear Control, 2023AbstractThis article elaborates the iterative learning mechanism for time‐varying system identification, and describes the learning algorithms that could achieve the consistent estimation for time‐varying parameters under persistent repetitive‐excitation conditions.
Mingxuan Sun, Guomin Zhong, Liming Wang
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Iterative Method for Exponential Damping Identification
Computer-Aided Civil and Infrastructure Engineering, 2014AbstractExponential damping is a new potential damping model for dynamic analysis of systems. This article outlines a complex mode procedure for identifying the exponential damping model and discusses its applicability and limitations. A new iterative method for relaxation factor is proposed, without using the full set of modal data, which is simple ...
Yuhua Pan, Yuanfeng Wang
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Identification based adaptive iterative learning controller
Asian Journal of Control, 2010AbstractIn recent years, more research in the control field has been in the area of self‐learning and adaptable systems, such as a robot that can teach itself to improve its performance. One of the more promising algorithms for self‐learning control systems is Iterative Learning Control (ILC), which is an algorithm capable of tracking a desired ...
Suhail Ashraf +2 more
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Iterative algorithm for autonomous star identification
IEEE Transactions on Aerospace and Electronic Systems, 2015An autonomous star identification algorithm is described in this study. This algorithm iteratively determines a catalog star, the distances of which to its neighbors are similar to those of a given sensor star and its neighboring stars, until a unique match is found.
Jian Li, Xinguo Wei, Guangjun Zhang
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Recursive–iterative identification method for power converters
Electrical Engineering, 2021Control of power electronic converters is an application area where system identification is necessary due to the implementation of suitable controllers. Thanks to the identification process, it is possible to obtain a suitable mathematical model of the converter, which can be used for the design of its control.
Peter Drgona +3 more
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Iterative learning identification for chaos communication
2008 27th Chinese Control Conference, 2008An approach for secure communication using chaotic systems is proposed, where the information signal to be transmitted is over a finite time interval. A parameter of the chaotic system is modulated by the information signal, and the mechanism for generating the transmitting signal is a pre-specified nonlinear function. Iterative learning identification
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Iterative Algorithms for Channel Identification Using Superimposed Pilots
2005 Australian Communications Theory Workshop, 2006Channel identification of a time-varying channel is considered using superimposed training. A sequence of known symbols with lower power is arithmetically added to the information symbols before modulation and transmission. The channel estimation is done exploiting the known superimposed data in the transmitted signal.
Varma, Angiras R. +3 more
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Multiscale iterative LBG clustering for SIMO channel identification
2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333), 2003This paper deals with the problem of channel identification for single input multiple output (SIMO) slow fading channels using clustering algorithms. The received data vectors of the SIMO model are spread in clusters because of the AWGN. Each cluster is centered around the ideal channel output labels without noise. Starting from the Markov SIMO channel
Laddomada, Massimiliano, Daneshgaran, F.
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An Iterative Identification Method for Linear Continuous-Time Systems
IEEE Transactions on Automatic Control, 2006This paper presents a novel approach to identification of continuous-time systems directly from the sampled I/O data based on trial iterations. The method achieves identification through ILC (iterative learning control) concepts in the presence of heavy measurement noise.
CAMPI, Marco, T. SUGIE, F. SAKAI
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Characterisation of the iterative integral parameter identification method
Medical & Biological Engineering & Computing, 2011Parameter identification methods are used to find optimal parameter values to fit models to measured data. The single integral method was defined as a simple and robust parameter identification method. However, the method did not necessarily converge to optimum parameter values. Thus, the iterative integral method (IIM) was developed.
Paul D, Docherty +2 more
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