基于遗传算法的数字滤波器的实现
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摘要
数字滤波器在数字信号处理中占有重要的地位,其优化设计一直受到广大研究者和工程人员的关注。而遗传算法作为一种寻求最优解的搜索算法,可用于模拟生物进化的过程。将遗传算法应用于滤波器优化设计,并通过可编程逻辑器件实现是当前信号处理的研究热点之一。
本文首先对遗传算法的机理及其运行过程进行深入分析,详细阐述了遗传操作的具体方案。通过一定的对比实验,突出了应用遗传算法优化设计后滤波器频率响应性能的优越性。
其次分别采用DSP Builder和VHDL模块设计实现FIR滤波器。在模块化设计中,完成对整个FIR滤波器的功能模块的划分,以及各个功能模块的VHDL语言编程设计,得到各个模块的仿真结果。通过输入一个合成波验证了FIR数字滤波器实现的良好效果。采用Altera公司的Cyclone飓风II代FPGA开发板,并构建A/D、D/A等外围电路,实现了“硬”滤波功能。将所设计的数字滤波器应用于音频信号的处理,以话音作为输入信号,进行了实际滤波的测试。
最后,本文对设计方案进行了分析,针对其不足之处提出了几种可能的解决方案。
Digital filter (DF) designs possess the important place in digital signal processing technology. DF optimization design has been concerned with by majority of researchers and engineers. As a search algorithm, genetic algorithm is aimed to find the optimal solution, thus it can be used to simulate the process of biological evolution. Designing and implementing DF by applying genetic algorithm and programmable logic device is the hot spot in signal processing technology nowadays.
Firstly in this dissertation, we conduct analysis of genetic algorithm mechanism and its manipulation operation in-depth. Then we compare by certain experiments; stand out the superiority of frequency response performance after optimization design by genetic algorithm method.
Secondly we follow by the use of DSP Builder and VHDL modular design to achieve FIR filter in two ways. In the modular design of FIR filter, we divide DF filter into several functional modules, and describe them with Very High Speed Integrated Circuit Hardware Description Language (VHDL).The simulation is done and a validation synthetic wave is used to verify its good result. Then we use a CycloneII FPGA development board of Altera Corporation and build the external circuit, including A/D, D/A circuit to implement the“hard”filter. Then the digital filter is used in audio signal processing. In actual test, we use voice as the input signal to verify its filtering effect.
Finally, we analysis the design scheme, and put forward several possible solutions for its inadequacies.
本文首先对遗传算法的机理及其运行过程进行深入分析,详细阐述了遗传操作的具体方案。通过一定的对比实验,突出了应用遗传算法优化设计后滤波器频率响应性能的优越性。
其次分别采用DSP Builder和VHDL模块设计实现FIR滤波器。在模块化设计中,完成对整个FIR滤波器的功能模块的划分,以及各个功能模块的VHDL语言编程设计,得到各个模块的仿真结果。通过输入一个合成波验证了FIR数字滤波器实现的良好效果。采用Altera公司的Cyclone飓风II代FPGA开发板,并构建A/D、D/A等外围电路,实现了“硬”滤波功能。将所设计的数字滤波器应用于音频信号的处理,以话音作为输入信号,进行了实际滤波的测试。
最后,本文对设计方案进行了分析,针对其不足之处提出了几种可能的解决方案。
Digital filter (DF) designs possess the important place in digital signal processing technology. DF optimization design has been concerned with by majority of researchers and engineers. As a search algorithm, genetic algorithm is aimed to find the optimal solution, thus it can be used to simulate the process of biological evolution. Designing and implementing DF by applying genetic algorithm and programmable logic device is the hot spot in signal processing technology nowadays.
Firstly in this dissertation, we conduct analysis of genetic algorithm mechanism and its manipulation operation in-depth. Then we compare by certain experiments; stand out the superiority of frequency response performance after optimization design by genetic algorithm method.
Secondly we follow by the use of DSP Builder and VHDL modular design to achieve FIR filter in two ways. In the modular design of FIR filter, we divide DF filter into several functional modules, and describe them with Very High Speed Integrated Circuit Hardware Description Language (VHDL).The simulation is done and a validation synthetic wave is used to verify its good result. Then we use a CycloneII FPGA development board of Altera Corporation and build the external circuit, including A/D, D/A circuit to implement the“hard”filter. Then the digital filter is used in audio signal processing. In actual test, we use voice as the input signal to verify its filtering effect.
Finally, we analysis the design scheme, and put forward several possible solutions for its inadequacies.
引文
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[17]王军伟,任良超.基于频率采样技术的FIR数字滤波器优化设计[J].仪器仪表学报,2005,26(8):475-477
[18]刘从新,吉培荣,曾维鲁.整体退火遗传算法用于FIR滤波器的频率采样设计[J].仪器仪表学报,2003,24(4):463-466
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[22]杨丽杰,崔葛瑾.基于FPGA的FIR滤波器设计方法的研究[J].东华大学学报,2006,32(6):93-96
[23] Kazi Shah Nawaz Ripon,Sam Kwong,K.F.Man.A real-coding jumping gene genetic algorithm(RJGGA) for multiobjective optimization[J].Information Sciences,2007,177:632-654
[24] Guoli Zhang,Hai Yan Lu,Gengyin Li,Hong Xie.A New Hybrid Real-Coded Genetic Algorithm and Application in Dynamic Economic Dispatch[J].IEEE,2006:3627-3632
[25]芦成刚.基于遗传算法的FIR滤波器优化设计[D].吉林:延边大学,2006
[26] Kwang Y.Lee.A Real-Coded Genetic Algorithm Involving a Hybrid Crossover Method for Power PlantControl System Design[J].IEEE,2002:1069-1074
[27]陈小平.进化计算及其应用研究[D].江苏:南京航空航天大学,2000
[28]刘守生.遗传算法与小波神经网络中若干问题的研究[D].江苏:南京航空航天大学,2004
[29]刘洋.遗传算法在数字滤波器设计中的应用研究[D].四川:电子科技大学,2004
[30]张文修,梁怡.遗传算法的数学基础[M].西安:西安交通大学出版社,2005
[31]徐宗本,高勇.遗传算法过早收敛现象的特征分析及其预防[J].中国科学,E辑,1996,26(4):364-375
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[35]程之刚,黎湘,庄钊文.一种基于多群体搜索的实数遗传算法[J].信号处理,2006,22(6):830-834
[36] Jong-Wook Kim, Sang Woo Kim, PooGyeon Park, Tae Joon Park. On the Similarities between binary-Coded GA and Real-Coded GA in Wide Search Space[J].IEEE,2002:681-686
[37]邱威.混合式遗传算法的研究与应用[D].黑龙江:哈尔滨工程大学,2005
[38]徐峥,炎士涛.基于父个体更新的自适应遗传算法[J].微计算机信息,2006,22(11):313-314
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[40] Kwang Y.Lee.A Real-Coded Genetic Algorithm Involving a Hybrid Crossover Method for Power Plant Control system Design[J].IEEE,2002:1069-1074
[41] Wei-Qing Xiong,You-Peng Zhang,Ping Wei.An Improved Real-Coded Genetic Algorithm[J].IEEE,2004:2361-2364
[42] Masato Takahashi,Hajime Kita.A Crossover Operator Using Independent Component Analysis for Real-Coded Genetic Algorithms[J].IEEE,2001:643-649
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