Journal ArticleDOI
Active mitigation of nonlinear noise Processes using a novel filtered-s LMS algorithm
Debi Prasad Das,Ganapati Panda +1 more
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TLDR
A novel filtered-s least mean square (FSLMS) algorithm based ANC structure, which functions as a nonlinear controller, is proposed in this paper and substantially reduces the number of operations compared to that of FSLMS as well as VFXLMS.Abstract:
In many practical applications the acoustic noise generated from dynamical systems is nonlinear and deterministic or stochastic, colored, and non-Gaussian. It has been reported that the linear techniques used to control such noise exhibit degradation in performance. In addition, the actuators of an active noise control (ANC) system very often have nonminimum-phase response. A linear controller under such situations can not model the inverse of the actuator, and hence yields poor performance. A novel filtered-s least mean square (FSLMS) algorithm based ANC structure, which functions as a nonlinear controller, is proposed in this paper. A fast implementation scheme of the FSLMS algorithm is also presented. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm and even performs better than the recently proposed Volterra filtered-x least mean square (VFXLMS) algorithm, in terms of mean square error (MSE), for active control of nonlinear noise processes. An evaluation of the computational requirements shows that the FSLMS algorithm offers a computational advantage over VFXLMS when the secondary path estimate is of length less than 6. However, the fast implementation of the FSLMS algorithm substantially reduces the number of operations compared to that of FSLMS as well as VFXLMS algorithm.read more
Citations
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Journal ArticleDOI
Recent Advances on Active Noise Control: Open Issues and Innovative Applications
TL;DR: Active noise control (ANC) was developed in the early 20th century to help reduce noise as discussed by the authors, but it is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications.
Journal ArticleDOI
A Generalized FLANN Filter for Nonlinear Active Noise Control
TL;DR: In this paper, an extension of the well-known FLANN filter using trigonometric expansions to include suitable cross-terms, i.e., products of input samples with different time shifts, was proposed.
Journal ArticleDOI
Efficient Adaptive Nonlinear Filters for Nonlinear Active Noise Control
Dayong Zhou,Victor DeBrunner +1 more
TL;DR: It is found that the computational complexity of NANC/NSP can be reduced even more using block-oriented nonlinear models, such as the Wiener, Hammerstein, or linear-nonlinear-linear (LNL) models for the NSP.
Journal ArticleDOI
Review: Advances in active noise control: A survey, with emphasis on recent nonlinear techniques
Nithin V. George,Ganapati Panda +1 more
TL;DR: The focus of this study is on the use of signal processing and some recent soft computing tools on the development of active noise control systems.
Journal ArticleDOI
Functional Link Adaptive Filters for Nonlinear Acoustic Echo Cancellation
Danilo Comminiello,Michele Scarpiniti,Luis A. Azpicueta-Ruiz,Jeronimo Arenas-Garcia,Aurelio Uncini +4 more
TL;DR: Experimental results show the effectiveness of the proposed FLAF-based architectures in nonlinear AEC scenarios, thus resulting an important solution to the modeling of nonlinear acoustic channels.
References
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Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
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Adaptive Signal Processing
Bernard Widrow,Samuel D. Stearns +1 more
TL;DR: This chapter discusses Adaptive Arrays and Adaptive Beamforming, as well as other Adaptive Algorithms and Structures, and discusses the Z-Transform in Adaptive Signal Processing.
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Adaptive pattern recognition and neural networks
TL;DR: This is a book that will show you even new to old thing, and when you are really dying of adaptive pattern recognition and neural networks, just pick this book; it will be right for you.
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