Active Mitigation of Nonlinear Noise Processes Using a Novel Filtered-s LMS Algorithm

Das, Debi Prasad; Panda, Ganapati

doi:10.1109/tsa.2003.822741

Cited by 232 publications

(113 citation statements)

References 8 publications

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“…The level of the residual noise is controlled by Least Mean Square (LMS) adaptive filters [1]. Narrowband and wideband noise sources are destructed by an ANC system based on the principle of destructive interference between the primary and secondary noise sources.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia

Ghosh

2018

Applied Sciences

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“…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%

“…H i (n) is the ith coefficient filter at time (n), Ψ is an orthogonal basis functional expansion. Several function expansions in the form of approximate and exact trigonometric, Legendre (L-FLANN), Chebyshev (C-FLANN), Hammerstein (H-FLANN), and Piecewise linear expansion (PWL) have been implemented [14][15][16]. The block diagram of the FSLMS based on FLNN is shown in Figure 4.…”

Section: Fslms Based On Flnnmentioning

confidence: 99%

“…Therefore, using low-cost sensors and actuators in an ANC system along with an efficient NANC algorithm can reduce the cost of the control system while ensuring good cancellation level. In the literature concerning NANC systems, two main models are commonly used to represent nonlinearities, namely, the truncated Volterra series [8,9,11,12] and functional link neural network (FLNN) [5,7,[11][12][13][14][15][16]. A study has suggested that the output error Bilinear filter is an efficient alternative to Volterra filter in reducing the saturation effect of the sensor and actuator [17].…”

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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“…Some available results show that feedback ANC systems based on adaptive finite impulse response (FIR) filter and filtered-x least-mean-square (FxLMS) algorithm work well for the cases with linear paths [14,15]. However, in practical applications, the nonlinearities in the primary path and secondary path may degrade the control performance [16]. Thus, the development of nonlinear controllers/filters is necessary.…”

Section: Introductionmentioning

confidence: 99%