Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

Abstract

Abstract. The method of least mean square (LMS) is the commonly used algorithm in Adaptive filter due to its simplicity and robustness in implementation. In Active Noise Control application, a filtered reference signal is used prior to LMS algorithm to overcome the constraint on stability and convergence performance of the system due to the existence of the auxiliary path. This is known as Filtered-X LMS algorithm. In conventional Filtered-X LMS algorithm, each filter weight is updated once on every audio sample. This paper proposes the improved version of Filtered-X LMS algorithm with the use of multiple iteration of filter weight on every sample of audio signal. The proposed work uses field programmable gate arrays to realize real-time simulation on hardware for the noise signal of 500 Hz. Results from the real-time hardware simulations have shown much faster error convergence and better adaptation performance for different selections of learning constant μ, as compared with the conventional method.

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

“…Study and implementation of seventeen of the best known LMS algorithms and their comparison on the practical aspects based on the study is carried out in [4]. The LMS algorithm is applied to an isolated hydropower station for the regulation of voltage and frequency, then a leaky factor is introduced and a further modified version is used to control the behavior of a Synchronous Reluctance Generator and in active noise control [5][6][7][8][9]. A new model is introduced to tackle the issue of the low convergence rate of the leaky algorithm [10].…”