| 改进的粒子群优化算法设计FIR低通数字滤波器 |
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| 引用本文： | 邵鹏,吴志健,彭虎,王映龙,周炫余.改进的粒子群优化算法设计FIR低通数字滤波器[J].计算机科学,2017,44(Z6):136-138, 156. |
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| 作者姓名： | 邵鹏 吴志健 彭虎 王映龙 周炫余 |
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| 作者单位： | 江西农业大学计算机与信息工程学院 南昌330045;武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072;九江学院信息科学与技术学院 九江332005,江西农业大学计算机与信息工程学院 南昌330045,武汉大学计算机学院 武汉430072 |
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| 基金项目： | 本文受国家自然科学基金(61070008,61364025),江西省自然科学基金项目(20132BAB201045),江西省教育厅科学技术项目(GJJ13729)资助 |
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| 摘 要： | 粒子群优化算法(PSO)因具有参数少、易于实现等优点,在解决优化问题时表现出很好的性能。有限长单位脉冲响应(FIR)数字滤波器因具有稳定的结构、易于实现等优点,在实际中有着很广泛的应用。因此,将基于三角函数因子的改进PSO算法(TFPSO)用于对FIR低通数字滤波器性能的优化,并将其与基于折射原理反向学习(refrPSO)、基于反向学习(OPSO)的PSO算法所设计的FIR低通数字滤波器的性能进行比较。在实验中构造出一种性能较好的适应值函数,以验证这几种改进的PSO算法所设计的FIR低通数字滤波器的性能。实验结果表明,基于三角函数因子的PSO算法滤波性能较差,而基于折射原理反向学习的PSO算法性能最佳。
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| 关 键 词： | 智能算法 粒子群优化算法 FIR数字滤波器 |
FIR Low-pass Digital Filter Design Using Improved PSO Algorithms |
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| SHAO Peng,WU Zhi-jian,PENG Hu,WANG Ying-long and ZHOU Xuan-yu.FIR Low-pass Digital Filter Design Using Improved PSO Algorithms[J].Computer Science,2017,44(Z6):136-138, 156. |
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| Authors: | SHAO Peng WU Zhi-jian PENG Hu WANG Ying-long and ZHOU Xuan-yu |
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| Affiliation: | School of Computer and Information Engineering,Jiangxi Agricultural University,Nanchang 330045,China;Computer School,Wuhan University,Wuhan 430072,China,Computer School,Wuhan University,Wuhan 430072,China,Computer School,Wuhan University,Wuhan 430072,China;School of Information Science and Technology,Jiujiang University,Jiujiang 332005,China,School of Computer and Information Engineering,Jiangxi Agricultural University,Nanchang 330045,China and Computer School,Wuhan University,Wuhan 430072,China |
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| Abstract: | Particle swarm optimization presents an excellent optimization performance when it solves some complex problems because of its advantages such as few parameters and easy implementation.Finite impulse response digital filters have some advantages such as stable structure and easy implementation,which make FIR low pass digital filters have a widely practical application.Therefore,in this paper,TFPSO was introduced to design FIR low pass digital filter and make a comparison with FIR low pass digital filters designed by refrPSO and OPSO.In the experiment,the excellent fitness function was proposed to test the performance of FIR low pass digital filters designed by several improved PSO algorithms.The experiment results show that refrPSO has an excellent filter performance and TFPSO has a weak filter performance. |
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| Keywords: | Intelligent algorithms Particle swarm optimization FIR digital filter |
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