Adaptive reduced feedback FLNN filter for active control of nonlinear noise processes

Authors:
Haiquan Zhao;Xiangping Zeng;Jiashu Zhang
Affiliations:
Si-Chuan province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China and School of Information Science and Technology, Southwest Jiaotong University ...;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;Si-Chuan province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China and School of Information Science and Technology, Southwest Jiaotong University ...
Venue:
Signal Processing
Year:
2010

Citing 15
Cited 6

Adaptive pattern recognition and neural networks

Adaptive pattern recognition and neural networks
A channel equalizer using reduced decision feedback Chebyshev functional link artificial neural networks

Information Sciences: an International Journal
Filtered-X affine projection algorithms for active noise control using Volterra filters

EURASIP Journal on Applied Signal Processing
Functional link neural network cascaded with Chebyshev orthogonal polynomial for nonlinear channel equalization

Signal Processing
Development and performance evaluation of FLANN based model for forecasting of stock markets

Expert Systems with Applications: An International Journal
Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel

IEEE Transactions on Neural Networks
Active control of nonlinear noise processes in a linear duct

IEEE Transactions on Signal Processing
Adaptive Volterra filters for active control of nonlinear noiseprocesses

IEEE Transactions on Signal Processing
Filtered-s LMS algorithm for multichannel active control of nonlinear noise processes

IEEE Transactions on Audio, Speech, and Language Processing
The Chebyshev-polynomials-based unified model neural networks forfunction approximation

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of nonlinear dynamic systems using functional linkartificial neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear channel equalization for QAM signal constellation usingartificial neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Functional-link net computing: theory, system architecture, and functionalities

Computer
Improved training of neural networks for the nonlinear active control of sound and vibration

IEEE Transactions on Neural Networks

Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization

Information Sciences: an International Journal
An adaptive decision feedback equalizer based on the combination of the FIR and FLNN

Digital Signal Processing
On the development of adaptive hybrid active noise control system for effective mitigation of nonlinear noise

Signal Processing
Review: Advances in active noise control: A survey, with emphasis on recent nonlinear techniques

Signal Processing
Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization

Expert Systems with Applications: An International Journal
Nonlinear feedback active noise control for broadband chaotic noise

Applied Soft Computing

Quantified Score

Hi-index	0.08

Visualization

Abstract

In actual nonlinear active noise control (NANC) systems, there often exist nonlinear distortions in such cases: the primary path may be nonlinear, the reference noise may exhibit nonlinear distortion, and the secondary path may have nonminimum-phase. To solve the problems of nonlinear distortions, two novel feedback adaptive filters based on the functional link neural network (FLNN) for NANC systems with low computational complexity are proposed in this paper, which are a feedback functional link neural network (FFLNN) and a reduced feedback functional link neural network (RFFLNN), respectively. To train the proposed nonlinear filters for NANC systems, a reduced complexity filtered-s least mean square (FSLMS) algorithm using filter bank approach is developed. The analysis of computational complexity shows that the RFFLNN adaptive filter involves less computation as compared to FFLNN and FLNN adaptive filters. Moreover, it is demonstrated through computer simulations for nonlinear noise processes that the RFFLNN adaptive filter outperforms FLNN and FFLNN in term of convergence speed and steady-state error. Furthermore, it is more effective in reducing nonlinear effects in NANC systems than other filters.