Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

Abstract:

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

• Periodical: Advanced Materials Research (Volumes 479-481)
• Pages: 1942-1945
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.479-481.1942
• Online since: February 2012
• Authors: Jie Zhang, Shi Qi Jiang
• Keywords: Adaptive Filter, Adaptive Noise Cancellation (ANC), LM Algorithm, Particle Swarm Optimization Algorithm (PSO)

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