Recursive adaptive sparse exponential functional link neural network for nonlinear AEC in impulsive noise environment

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.