Design of Adaptive Exponential Functional Link Network-Based Nonlinear Filters

A novel nonlinear filter, which incorporates the concept of exponential sinusoidal models into nonlinear filters based on functional link networks (FLNs) has been developed in this paper. The proposed filter is designed to provide improved convergence characteristics over traditional FLN filters. The conventional trigonometric FLN may be considered as a special case of the proposed adaptive exponential FLN (AEFLN). An adaptive exponential least mean square (AELMS) algorithm has been derived and the same has been successfully applied for identification of a couple of nonlinear plants. The AEFLN-based nonlinear active noise control (ANC) system has also been designed and an adaptive exponential filtered-s least mean square (AEFsLMS) algorithm has been developed to update the weights as well as the exponential factor. Simulation study has revealed the improved noise mitigation offered by the AEFLN-based nonlinear ANC system.