Accurate simulation and modeling of complex multi-port LTI systems can be computationally very expensive and resource-demanding. Adaptive system identification techniques, that combine adaptive sampling and modeling algorithms, can be used to minimize the number of costly data samples, and to build accurate broadband models in a limited amount of time. This paper gives a survey of several recent advances in the world of deterministic system identification, based on sparse and costly data
This paper considers the problem of identifying continuous-time linear time-invariant systems in fre...
This paper presents the experimental development of software and hardware configuration to implement...
This paper presents a new adaptive technique for the identification of a linear system driven by whi...
In this paper, we develop the adaptive algorithm for system identification where the model is sparse...
Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advant...
We propose a new low complexity and fast converging frequency-domain adaptive algorithm for sparse s...
Identification is a powerful technique used to build accurate models of system from noisy data. The ...
Abstract—In order to improve the performance of Least Mean Square (LMS) based system identification ...
This paper considers the use of sparse estimation techniques to determine an appropriate set of basi...
Low complexity of a system model is essential for its use in real-time applications. However, sparse...
Low complexity of a system model is essential for its use in real-time applications. However, sparse...
By minimising the weighted Wilcoxon norm instead of the weighted Euclidean norm as the cost function...
In the traditional system identification techniques, a priori model structure is widely assumed to b...
International audienceArmed with structures, group sparsity can be exploited to extraordinarily impr...
The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to th...
This paper considers the problem of identifying continuous-time linear time-invariant systems in fre...
This paper presents the experimental development of software and hardware configuration to implement...
This paper presents a new adaptive technique for the identification of a linear system driven by whi...
In this paper, we develop the adaptive algorithm for system identification where the model is sparse...
Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advant...
We propose a new low complexity and fast converging frequency-domain adaptive algorithm for sparse s...
Identification is a powerful technique used to build accurate models of system from noisy data. The ...
Abstract—In order to improve the performance of Least Mean Square (LMS) based system identification ...
This paper considers the use of sparse estimation techniques to determine an appropriate set of basi...
Low complexity of a system model is essential for its use in real-time applications. However, sparse...
Low complexity of a system model is essential for its use in real-time applications. However, sparse...
By minimising the weighted Wilcoxon norm instead of the weighted Euclidean norm as the cost function...
In the traditional system identification techniques, a priori model structure is widely assumed to b...
International audienceArmed with structures, group sparsity can be exploited to extraordinarily impr...
The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to th...
This paper considers the problem of identifying continuous-time linear time-invariant systems in fre...
This paper presents the experimental development of software and hardware configuration to implement...
This paper presents a new adaptive technique for the identification of a linear system driven by whi...