ON THE DESIGN OF DYNAMIC ADAPTIVE EXPONENTIAL LINEAR- IN-THE-PARAMETERS NONLINEAR FILTERS FOR ACTIVE NOISE CONTROL Trisrota Deb Indian Institute of Technology Gandhinagar, Gujarat, India email: trisrota.deb@iitgn.ac.in Dwaipayan Ray Indian Institute of Technology Gandhinagar, Gujarat, India email: dwaipayan.ray@iitgn.ac.in Nithin V. George Indian Institute of Technology Gandhinagar, Gujarat, India email: nithin@iitgn.ac.in Adaptive exponential functional link network (AEFLN) is a recently developed linear-in-the-parameters nonlinear filter, which provides significantly better convergence performance over other traditional linear-in-the-parameters nonlinear filters. To further improve the convergence characteristics of AE- FLN, a variable step-size AEFLN (VSS-AEFLN) is proposed in this paper. An adaptive exponential variable step-size least mean square (AEVSS-LMS) algorithm is developed, and the same is tested on modeling benchmark nonlinear plants. Following the above formulation, a VSS-AEFLN-based nonlinear active noise control (ANC) system is designed, and an adaptive exponential filtered-s vari- able step-size least mean square (AEFsVSS-LMS) algorithm is also developed for improved noise mitigation. Simulation results show that the convergence performance of the proposed algorithms, for system identification and ANC systems, is superior to the state-of-the-art linear-in-the-parameter nonlinear adaptive filters. Keywords: Active noise control (ANC), nonlinear adaptive filter, functional link neural network (FLN), nonlinear system identification, and variable step-size approach. 1. Introduction Lower computational load and simple structure of single-layered nonlinear filter have made it a promising candidate for system identification in the presence of nonlinearities in the system [1]. One of the important areas where these filters are often used is the active noise control (ANC). In general, the experimental set-up of a traditional ANC system consists of three electro-acoustic elements, namely, (i) a reference microphone that senses the noise to be cancelled, (ii) a loudspeaker that creates the anti-noise, and (iii) an error microphone that measures the degree of noise cancellation achieved. The functionality of the cancelling loudspeaker is governed by a finite impulse response (FIR) based adaptive filter, where 1