Individually Weighted Modified Logarithmic Hyperbolic Sine Curvelet Based Recursive FLN for Nonlinear System Identification

Lately, an adaptive exponential functional link network (AEFLN) involving exponential terms integrated with trigonometric functional expansion is being introduced as a linear-in-the-parameters nonlinear filter. However, they exhibit degraded efficacy in lieu of non-Gaussian or impulsive noise interference. Therefore, to enhance the nonlinear modelling capability, here is a modified logarithmic hyperbolic sine cost function in amalgamation with the adaptive recursive exponential functional link network. In conjugation with this, a sparsity constraint motivated by a curvelet-dependent notion is employed in the suggested approach. Therefore, this paper presents an individually weighted modified logarithmic hyperbolic sine curvelet-based recursive exponential FLN (IMLSC-REF) for robust sparse nonlinear system identification. An individually weighted adaptation gain is imparted to several coefficients corresponding to the nonlinear adaptive model for accelerating the convergence rate. The weight update rule and the maximum criteria for the convergence factor are being further derived. Exhaustive simulation studies profess the effectiveness of the introduced algorithm in case of varied nonlinearity and for identifying as well as modelling the physical path of the acoustic feedback phenomenon of a behind-the-ear (BTE) hearing aid.