Enhanced LMS Algorithm with Application in Noise Cancellation
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Resource Overview
An improved LMS algorithm and its application in noise cancellation. Building upon traditional fixed-step and variable-step LMS algorithms, this enhanced variable-step method utilizes exponential cubic instantaneous error magnitude with a forgetting factor for simultaneous step-size adjustment, effectively balancing convergence speed and steady-state error trade-offs.
Detailed Documentation
In this paper, we explore an enhanced LMS algorithm and its application in noise cancellation systems. Our analysis begins with fundamental examinations of conventional fixed-step LMS and variable-step LMS algorithms, revealing inherent limitations in resolving the conflict between convergence speed and steady-state error. Consequently, we propose an improved variable-step LMS algorithm that employs exponential cubic instantaneous error magnitude combined with a forgetting factor to dynamically adjust the step size. This dual-mechanism approach demonstrates superior performance in noise cancellation scenarios by maintaining rapid convergence while minimizing residual error, providing new perspectives for research and practical implementations in adaptive filtering applications.
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