吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 907-912.doi: 10.13229/j.cnki.jdxbgxb201503032

• • 上一篇    下一篇

基于反向学习的粒子群算法对线性相位低通FIR滤波器的优化

邵鹏1, 2, 吴志健1, 2, 周炫余2   

  1. 1.武汉大学 软件工程国家重点实验室,武汉 430072;
    2.武汉大学 计算机学院,武汉 430072
  • 收稿日期:2013-09-23 出版日期:2015-05-01 发布日期:2015-05-01
  • 通讯作者: 吴志健(1963-),男,教授,博士生导师.研究方向:智能计算,反问题,并行计算. E-mail:sp198310@163.com
  • 作者简介:邵鹏(1983-),男,博士研究生.研究方向:智能计算,智能信息处理.
  • 基金资助:
    国家自然科学基金项目(61070008,70971043); 武汉大学软件工程国家重点实验室开放基金项目(SKLSE2012-09-19); 中央高校基本科研业务费专项项目(2012211020205); 江西省教育厅科学技术项目(GJJ13729)

Particle swarm optimization algorithm based on opposite learning for linear phase low-pass FIR filter optimization

SHAO Peng1, 2, WU Zhi-jian1, 2, ZHOU Xuan-yu2   

  1. 1.State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, China;
    2.Computer School, Wuhan University, Wuhan 430072, China
  • Received:2013-09-23 Online:2015-05-01 Published:2015-05-01

摘要: 将一种基于反向学习的改进粒子群优化算法(OPPSO)应用于设计具有线性相位的低通FIR数字滤波器。该算法通过求解粒子位置的反向解来增加找到最优解的概率,从而求得一组使所述滤波器最优的参数组合。同时,在试验中引入一种更具有现实意义的接近于理想滤波器的零相位滤波器作为对比。试验结果表明,该算法在设计线性相位低通FIR数字滤波器上具有良好的优化效果。

关键词: 人工智能, 粒子群优化算法, 有限脉冲响应, 数字滤波器, 反向学习

Abstract: Finite Impulse Response (FIR) digital filter design is actually the design of unit impulse response coefficients combination, which is essentially a combination of multi-parameter optimization problem. On this basis, an improved particle swarm optimization algorithm based on opposite learning strategy (OPPSO) is applied to design linear phase low-pass FIR digital filter. OPPSO increases the probability to find the optimal solution by solving the inverse solution of the particle position, thus, to find a set of optimal combination of parameters of the filter. Meanwhile, a more realistic sense of zero phase filters close to the ideal filter is introduced into the experiment for comparison. Experimental results show that the proposed algorithm has good optimal results for linear phase low-pass FIR digital filter design.

Key words: artificial intelligence, particle swarm optimization, finite impulse response, digital filter, opposite learning

中图分类号: 

  • TP18
[1] Li K,Liu Y. The FIR window function design based on evolutionary algorithm[C]∥2011 International Conference on Mechatronic Science, Electric Engineering and Computer,Jilin,2011:1797-1800.
[2] 程佩青.数字信号处理[M].北京:清华大学出版社,2009.
[3] Zhao Z K, Gao H Y, Liu Y Q. Chaotic particle swarm optimization for FIR filter design[C]∥2011 International Conference on Electrical and Control Engineering,Yichang,2011:2058-2061.
[4] Ahmad S U,Antoniou A. A genetic algorithm approach for fractional delay FIR filters[C]∥IEEE International Symposium on Circuits and Systems,Island of Kos,2006:2517-2520.
[5] Mastorakis N E,Gonos I F,Swamy M N S. Design of two dimensional recursive filters using genetic algorithms[J]. IEEE Transaction on Circuits and Systems-I: Fundamental Theory and Applications, 2003,50(5):634-639.
[6] Mercier P,Kilambi S M,Nowrouzian B. Optimization of FRM FIR digital filters over CSD and CDBNS multiplier coefficient spaces employing a novel genetic algorithm[J]. Journal of Computers,2007,9(2):20-31.
[7] An-xin Z, Ping L, Jian-jun L. The design of FIR filter based on APA genetic algorithms[C]∥2011 International Conference on Mechatronic Science, Electric Engineering and Computer,Jilin,2011:1118-1121.
[8] 李辉,张安,赵敏,等. 粒子群优化算法在FIR数字滤波器设计中的应用[J]. 电子学报,2005,33(7):1338-1341.
Li Hui, Zhang An, Zhao Min, et al. Particle swarm optimization algorithm for FIR digital filters design[J]. Acta Electronica Sinica,2005,33(7):1338-1341.
[9] Rajib Kar,Durbadal Mandal,Sangeeta Mondal, et al. Craziness based particle swarm optimization algorithm for FIR band stop filter design[J]. Swarm and Evolutionary Computation ,2012,7:58-64.
[10] Mondal S,Chakraborty D,Kar R, et al. Novel particle swarm optimization for high pass FIR filter design[C]∥2012 IEEE Symposium on Humanities, Science and Engineering Research,Kuala Lumpur,2012:413-418.
[11] Kennedy J, Eberhart R. Particle swarm optimization[C]∥Proceedings of IEEE International Conference on Neural Networks,1995,4(2):1942-1948.
[12] Tizhoosh H R. Opposition-based learning: a new scheme for machine intelligence[C]∥CIMCA/IAWTI,Vienna, Austria,2005:695-701.
[13] 寇晓丽,刘三阳.基于模拟退火的粒子群算法求解约束优化问题[J].吉林大学学报:工学版,2007,37(1):136-140.
Kou Xiao-li, Liu San-yang. Particle swarm algorithm based on simulated annealing to solve constrained optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2007, 37(1):136-140.
[15] 纪跃波,秦树人,汤宝平. 零相位数字滤波器[J].重庆大学学报:自然科学版,2000,23(16):4-7.
Ji Yue-bo,Qin Shu-ren,Tang Bao-ping. Digital filtering with zero phase error[J]. Journal of Chongqing University(Natural Science Edition),2000,23(16):4-7.
[16] Luitel B, Venayagamoorthy G K. Differential evolution particle swarm optimization for digital filter design[C]∥2008 IEEE Congress on Evolutionary Computation,Hong Kong,2008:3954-3961.
[17] Ababneh J I, Bataineh M H. Linear phase FIR filter design using particle swarm optimization and genetic algorithms[J]. Digital Signal Processing,2008,18(4):657-668.
[18] Sarangi A,Mahapatra R K,Panigrahi S P. DEPSO and PSO-QI in digital filter design[J]. Expert Systems with Applications, 2011,38(9):10966-10973.
[19] Mandal S,Ghoshal S P,Kar R,et al. FIR band stop filter optimization by improved particle swarm optimization[C]∥2011 World Congress on Information and Communication Technologies,Mumbai,2011:699-704.
[1] 董飒, 刘大有, 欧阳若川, 朱允刚, 李丽娜. 引入二阶马尔可夫假设的逻辑回归异质性网络分类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1571-1577.
[2] 顾海军, 田雅倩, 崔莹. 基于行为语言的智能交互代理[J]. 吉林大学学报(工学版), 2018, 48(5): 1578-1585.
[3] 王旭, 欧阳继红, 陈桂芬. 基于垂直维序列动态时间规整方法的图相似度度量[J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[4] 张浩, 占萌苹, 郭刘香, 李誌, 刘元宁, 张春鹤, 常浩武, 王志强. 基于高通量数据的人体外源性植物miRNA跨界调控建模[J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[5] 黄岚, 纪林影, 姚刚, 翟睿峰, 白天. 面向误诊提示的疾病-症状语义网构建[J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[6] 李雄飞, 冯婷婷, 骆实, 张小利. 基于递归神经网络的自动作曲算法[J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7] 刘杰, 张平, 高万夫. 基于条件相关的特征选择方法[J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[8] 蔡振闹, 吕信恩, 陈慧灵. 基于反向细菌优化支持向量机的躯体化障碍预测模型[J]. 吉林大学学报(工学版), 2018, 48(3): 936-942.
[9] 王旭, 欧阳继红, 陈桂芬. 基于多重序列所有公共子序列的启发式算法度量多图的相似度[J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[10] 杨欣, 夏斯军, 刘冬雪, 费树岷, 胡银记. 跟踪-学习-检测框架下改进加速梯度的目标跟踪[J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[11] 刘雪娟, 袁家斌, 许娟, 段博佳. 量子k-means算法[J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[12] 曲慧雁, 赵伟, 秦爱红. 基于优化算子的快速碰撞检测算法[J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[13] 李嘉菲, 孙小玉. 基于谱分解的不确定数据聚类方法[J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[14] 刘颖, 张凯, 于向军. 基于代理模型的中空轴式大型静压轴承多目标优化[J]. 吉林大学学报(工学版), 2017, 47(4): 1130-1137.
[15] 邵克勇, 陈丰, 王婷婷, 王季驰, 周立朋. 无平衡点分数阶混沌系统全状态自适应控制[J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!