Fractional-order LMS filter Based on Chaotic Particle Swarm - Wan, Honglin; Sheng, Hu; Yang, Jingxin (2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), 2021)
In the traditional LMS adaptive algorithm, not only is the step factor a fixed value, but there is also an irreconcilable conflict between the convergence speed and the steady-state error. Although fractional-order LMS algorithms have been proposed to improve the convergence characteristics, the step factor is still a fixed value, which is prone to divergence when not set properly. In this paper, the fractional-order LMS algorithm is improved and combined with the chaotic particle swarm algorithm. The fractional-order LMS algorithm is combined with a chaotic particle swarm algorithm, and the fixed step factor value in the fractional-order LMS algorithm is changed to a dynamically varying step factor. The results show that the convergence speed and steady-state error of the fractional-order LMS filter are effectively improved by adding the chaotic particle swarm.