Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 29, Issue 9, Pages 4314-4323Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2017.2761259
Keywords
Functional link neural network; impulsive noise; nonlinear acoustic echo cancellation; sparse adaptive filter
Categories
Funding
- Australian Research Council [DP120104986]
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Recently, an adaptive exponential trigonometric functional link neural network (AETFLN) architecture has been introduced to enhance the nonlinear processing capability of the trigonometric functional link neural network (TFLN). However, it suffers from slow convergence speed, heavy computational burden, and poor robustness to noise in nonlinear acoustic echo cancellation, especially in the double-talk scenario. To reduce its computational complexity and improve its robustness against impulsive noise, this paper develops a recursive adaptive sparse exponential TFLN (RASETFLN). Based on sparse representations of functional links, the robust proportionate adaptive algorithm is deduced from the robust cost function over the RASETFLN in impulsive noise environments. Theoretical analysis shows that the proposed RASETFLN is stable under certain conditions. Finally, computer simulations illustrate that the proposed RASETFLN achieves much improved performance over the AETFLN in several nonlinear scenarios in terms of convergence rate, steady-state error, and robustness against noise.
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