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Research and Application of the Particle Swarm Optimization in Adaptive Notch Filter Design

机译:自适应陷波滤波器设计中粒子群优化的研究与应用

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Traditional adaptive notch filter based on the LMS algorithm is so serious affected by the step-size and other parameters that the learning curve isn’t very well. Inspired by analyzing effect of step-size in adaptive notch filter based on the LMS algorithm, the PSO algorithm is applied to design the adaptive notch filter for the first time in this paper. The PSO algorithm is not depended on any parameters. On the other hand, the PSO algorithm has many advantages of simplicity, computational efficiency, and good performance under a variety of operating conditions. The simulations shows that the adaptive notch filter based on the PSO algorithm is better than classic adaptive notch filter based on the LMS algorithm, convergence speed and the robustness. As a result, it is efficient and it has some value in the engineering.
机译:基于LMS算法的传统自适应陷波滤波器是由学习曲线的阶梯大小和其他参数影响的严重影响。通过基于LMS算法的自适应陷波滤波器的分析效果来分析阶梯大小的影响,应用PSO算法在本文中首次设计自适应陷波滤波器。 PSO算法不依赖于任何参数。另一方面,PSO算法在各种操作条件下具有简单性,计算效率和良好性能的优点。该模拟表明,基于PSO算法的自适应陷波滤波器优于基于LMS算法,收敛速度和鲁棒性的经典自适应陷波滤波器。结果,它有效,它在工程中具有一些价值。

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