Accuracy and Performance Evaluation in the Genetic Optimization of Nonlinear Systems for Active Noise Control

Russo, F.; Sicuranza, Giovanni L.

doi:10.1109/tim.2007.899911

Cited by 46 publications

(33 citation statements)

References 24 publications

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“…For single and multichannel systems, a combined approach of adaptive filters for ANC had been proposed [26]. For the proposed work, in between the adaptive filter output and the error sensor signal output, the secondary path is considered.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia

Ghosh

2018

Applied Sciences

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“…Typically, the saturation characteristic of a loudspeaker is modelled by the sigmoid hyperbolic tangent function. In previous study, the nonlinear effect of the power amplifier and loudspeaker has been modelled as a nonlinear filter without memory [1] which is defined as r(n) = 0.66 tanh 1.5y(n) .…”

Section: Sensors Actuatorsmentioning

confidence: 99%

“…In ANC system, the noise that is generated from a dynamic system may be nonlinear and deterministic chaotic rather than stochastic, white, or tonal noise processes [1,5,7,8,12,14,[16][17][18]. Research has shown that the noise measured from a ventilation fan exhibit chaotic behaviour [28].…”

Section: Reference Noisementioning

confidence: 99%

“…Transportation and road noise from motor vehicles, aircraft, and rail are major contributors to environmental noise [1]. An example of transportation noise is the ambient noise produced by hybrid electrical vehicles, which originates from the alternator and cooling fans of the engine and battery packs [2].…”

Section: Introductionmentioning

confidence: 99%

“…Linear ANC techniques are limited in that the system exhibits performance degradation when dealing with nonlinearities [1,[8][9][10]. Nonlinearity is a concern in ANC application where low-cost devices such as amplifiers, sensors, and actuators exhibit nonlinear distortion.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms

Sahib

Kamil

2011

ISRN Mechanical Engineering

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Research on nonlinear active noise control (NANC) revolves around the investigation of the sources of nonlinearity as well as the performance and computational load of the nonlinear algorithms. The nonlinear sources could originate from the noise process, primary and secondary propagation paths, and actuators consisting of loudspeaker, microphone or amplifier. Several NANCs including Volterra filtered-x least mean square (VFXLMS), bilinear filtered-x least mean square (BFXLMS), and filtereds least mean square (FSLMS) have been utilized to overcome these nonlinearities effects. However, the relative performance and computational complexities of these algorithm in comparison to FXLMS algorithm have not been carefully studied. In this paper, systematic comparisons of the FXLMS against the nonlinear algorithms are evaluated in overcoming various nonlinearity sources. The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. Computer simulations show that the performance of the FXLMS is more than 80% of the most effective nonlinear algorithm for each type of nonlinearity sources at the fraction of computational load. The simulation results also suggest that it is more advantageous to use FXLMS for practical implementation of NANC.

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Nonlinear FXLMS algorithm for active noise control systems with saturation nonlinearity

Sahib

Kamil

Marhaban

2012

IEEJ Transactions Elec Engng

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In active noise control (ANC) applications, the saturation effect of the loudspeaker in the secondary path is considered as the most serious problem that could degrade performance of standard filtered-x least mean square (FXLMS) control algorithm. When the loudspeaker exhibits nonlinearities, the linear modeling approach fails to identify the secondary path accurately. In the literature, the nonlinear FXLMS (NLFXLMS) algorithm has been proposed to update the ANC controller with a blockoriented secondary path model. This model consists of nonlinear and linear filters whereby the nonlinear part which represents the saturation effect of the amplifier-loudspeaker system is modeled by a scaled error function (SEF). The NLFXLMS algorithm requires an exact copy of the linear and nonlinear models of the secondary path. However, NLFXLMS cannot be implemented in real time because the modeling of the SEF cannot be realized. In this paper, a new method to model the secondary path using the Hammerstein model structure and tangential hyperbolic function (THF) is proposed. The THF can represent the SEF to a certain degree of accuracy. Furthermore, the modeling of the THF can be realized using least mean square (LMS) algorithm and utilized in the NLFXLMS control scheme. Simulation results show that the performance of the THF-based NLFXLMS algorithm is comparable with the SEF-based NLFXLMS.

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Accuracy and Performance Evaluation in the Genetic Optimization of Nonlinear Systems for Active Noise Control

Cited by 46 publications

References 24 publications

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms

Nonlinear FXLMS algorithm for active noise control systems with saturation nonlinearity

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