An adaptive combination of adaptive filters for plant identification

Martínez‐Ramón, Manel; Arenas‐García, Jerónimo; Navia-Vázquez, Ángel; Figueiras-Vidal, Anı́bal R.

doi:10.1109/icdsp.2002.1028307

Cited by 65 publications

(56 citation statements)

References 6 publications

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“…Taking advantage of such sparse prior information can improve the identifying performance. However, the proposed two combination structure filters [2] [4] do not exploit such information due to the fact that they adopted standard LMS filters. Thus, there is a great interest in exploiting the sparse structure information to improve the filtering performance in sparse systems.…”

Section: A Background and Motivationmentioning

confidence: 99%

“…The first adaptive filter uses larger step-size than second filter so that the combination filter can achieve a good/fair tradeoff between convergence speed and steady-state MSE. An improved filter using convex combination of two fixed step-size based standard LMS (CC-LMS) filters was first proposed [2] and later, its steady-state performance was analyzed in [3]. Moreover, an affine combination of two standard LMS (AC-LMS) filters was also proposed and was studied via transient MSE in [4].…”

Section: A Background and Motivationmentioning

confidence: 99%

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Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters

Gui

Kumagai

Mehbodniya

et al. 2014

2014 IEEE 79th Vehicular Technology Conference (VTC Spring)

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Abstract-Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming. One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter. However, the adoption of only one sparse LMS filter cannot simultaneously achieve fast convergence speed and small steady-state mean state deviation (MSD). Unlike the conventional method, we propose an improved ASSI method using affine combination of two sparse LMS filters to simultaneously achieving fast convergence and low steady-state MSD. First, problem formulation and standard affine combination of LMS filters are introduced. Then an approximate optimum affine combiner is adopted for the proposed filter according to stochastic gradient search method. Later, to verify the proposed filter for ASSI, computer simulations are provided to confirm effectiveness of the proposed filter which can achieve better estimation performance than the conventional one and standard affine combination of LMS filters.

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Section: A Background and Motivationmentioning

confidence: 99%

Section: A Background and Motivationmentioning

confidence: 99%

Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters

Gui

Kumagai

Mehbodniya

et al. 2014

2014 IEEE 79th Vehicular Technology Conference (VTC Spring)

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show abstract

“…The second is a slow filter (small m 2 ) that will provide a very good approximation of the plant in stationary (or slowly changing) situations. This idea was presented as a preliminary version in [17]. Here, we add several algorithmic changes to circumvent the serious limitations of that version.…”

Section: Adaptive Combination Of Lms Filters: the Clms Algorithmmentioning

confidence: 99%

Plant identification via adaptive combination of transversal filters

et al. 2006

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For least mean square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least squares (RLS) filters..

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“…Recently there has been an interest in a combination scheme that is able to optimize the trade-off between convergence speed and steady state error Martinez-Ramon et al (2002). The scheme consists of two adaptive filters that are simultaneously applied to the same inputs as depicted in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Transient Analysis of a Combination of Two Adaptive Filters

Trump¹

2011

Adaptive Filtering

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An adaptive combination of adaptive filters for plant identification

Cited by 65 publications

References 6 publications

Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters

Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters

Plant identification via adaptive combination of transversal filters

Transient Analysis of a Combination of Two Adaptive Filters

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