| 自适应FIR滤波器的算法研究 |
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| 引用本文： | 王静宜,高振斌,曾祥烨.自适应FIR滤波器的算法研究[J].河北工业大学学报,2010,39(1). |
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| 作者姓名： | 王静宜 高振斌 曾祥烨 |
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| 作者单位： | 河北工业大学,信息工程学院,天津,300401 |
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| 基金项目： | 河北省自然科学基金(F2008000116);;河北省教育厅科学研究计划(2009423) |
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| 摘 要： | 针对有限脉冲响应(finite impulse response,FIR)数字滤波器结构的优化问题,通过在系统辨识模型中的应用,将粒子群优化算法(particle swarm optimization,PSO)与传统自适应最小均方(LMS)算法和递归最小二乘(RLS)算法仿真结果进行对比.实验结果表明了3种算法在不同的应用环境和条件下具有各自的优越性,为今后自适应滤波算法的选择提供了一定的参考信息.仿真结果中PSO算法在低阶滤波器应用中具有快速的收敛性、较高的计算精度以及低复杂度等特点,可以将其作为均衡算法对偏振膜色散进行补偿,为这一研究方向提供了新的思路和方法.
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| 关 键 词： | 信号处理 自适应滤波 系统辨识 粒子群优化算法 最小均方算法 递归最小二乘算法 |
Study on Adaptive FIR Filtering Algorithms |
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| WANG Jing-yi,GAO Zhen-bin,ZENG Xiang-ye.Study on Adaptive FIR Filtering Algorithms[J].Journal of Hebei University of Technology,2010,39(1). |
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| Authors: | WANG Jing-yi GAO Zhen-bin ZENG Xiang-ye |
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| Affiliation: | School of Information Engineering;Hebei University of Technology;Tianjin 300401;China |
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| Abstract: | With optimization design of FIR digital filter,methods based on particles swarm optimization (PSO),LMS and RLS are used for system identification model. Simulation results demonstrate effectiveness and superiority of every method in application to different circumstance. It provides some references in application of adaptive filtering algorithms. From the experimental results we can also see that the PSO algorithm has the merits of rapid convergence,high accuracy and low complexity in low-order filter. It c... |
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| Keywords: | signal processing adaptive filtering system identification particle swarm optimization(PSO) least-mean square(LMS) recursive least-square(RLS) |
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