On the BIBO Stability Condition of Adaptive Recursive FLANN Filters With Application to Nonlinear Active Noise Control

Sicuranza, Giovanni L.; Carini, Alberto

doi:10.1109/tasl.2011.2159788

Cited by 76 publications

(37 citation statements)

References 30 publications

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“…A population-based refutable algorithm, developed by Kennedy and Eberhart, is Particle Swarm Optimization [31]. This algorithm is developed based on the inspiration of the blocking behavior of birds or the schooling behavior of fish.…”

Section: Hybrid Psoff Algorithmmentioning

confidence: 99%

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia

Ghosh

2018

Applied Sciences

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Active noise control is an efficient technique for noise cancellation of the system, which has been defined in this paper with the aid of Modified Filtered-s Least Mean Square (MFsLMS) algorithm. The Hybrid Particle Swarm Optimization and Firefly (HPSOFF) algorithm are used to identify the stability factor of the MFsLMS algorithm. The computational difficulty of the modified algorithm is reduced when compared with the original Filtered-s Least Mean Square (FsLMS) algorithm. The noise sources are removed from the signal and it is compared with the existing FsLMS algorithm. The performance of the system is established with the normalized mean square error for two different types of noises. The proposed method has also been compared with the existing algorithms for the same purposes.

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Section: Hybrid Psoff Algorithmmentioning

confidence: 99%

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia

Ghosh

2018

Applied Sciences

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“…More recently, an adaptive control algorithm was developed in [20] for general function expansion nonlinear filters, based on the so-called virtual SP filter, which is related to the gradient of the SP. A FULMS algorithm for recursive FLANN filters with the SP modeled as a Hammerstein filter is proposed in [22].…”

Section: Nanc Methods For Nonlinear Secondary Pathsmentioning

confidence: 99%

A dual filtering scheme for nonlinear active noise control

Delvecchio

Piroddi

2013

Adaptive Control & Signal

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Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered-x least mean squares (LMS) or filtered-u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness.

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“…A few non-linear ANC schemes, which uses an adaptive non-linear filter as the controller, has been recently reported in literature to achieve noise cancellations in scenarios in which non-linearities exist in the ANC system [7]- [11]. The popular among them are the adaptive Volterra filter trained using a Volterra FxLMS (VFxLMS) and the functional link artificial neural network (FLANN) updated using a filtered-s least mean square (FsLMS) algorithm [12]- [16].…”

Section: Introductionmentioning

confidence: 99%

Collaborative adaptive exponential linear-in-the-parameters nonlinear filters

Patel

Pradhan

George

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-An adaptive exponential functional link artificial neural network (AEFLANN) based active noise control (ANC) system trained using a collaborative learning scheme has been designed in this paper. In the proposed approach, separate learning mechanism is used for updating the weights of the linear portion of the AEFLANN and its non-linear section. The outputs of the linear and non-linear sections are suitably combined and the update mechanism involves the update of weights of linear and non-linear portions, the combination parameter and the adaptive exponential factor. Simulation study shows enhanced noise cancellation in comparison with other non-linear ANC schemes compared.Index Terms-Active noise control, functional link artificial neural network, noise cancellation, non-linear filter.

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On the BIBO Stability Condition of Adaptive Recursive FLANN Filters With Application to Nonlinear Active Noise Control

Cited by 76 publications

References 30 publications

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

A dual filtering scheme for nonlinear active noise control

Collaborative adaptive exponential linear-in-the-parameters nonlinear filters

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