摘要
采用改进遗传量子算法(IGQA)进行FIR数字滤波器的优化设计,将滤波器的过渡带样本值作为变量进行优化,解决了传统方法(查表法)不能保证数据最优的问题。针对遗传量子算法(GQA)在优化连续多峰函数时易出现早熟的问题,提出一种改进遗传量子算法(IGQA),典型函数测试表明,IGQA的性能优于GQA和其它几种遗传算法,收敛速度快,全局寻优能力强,能有效地克服早熟现象。采用IGQA优化设计的FIR数字低通和带通滤波器的性能较查表法得到了很大改善。
Improved genetic quantum algorithm (IGQA) is used to design FIR digital filters. Samples in transitions of filters are optimized, which solves the problem that values obtained by look-up table method (LTUM) are not optimums. Aiming at prematureness appearing easily when GQA is used to optimize continuous functions with many peaks, IGQA is proposed.The results of testing typical function and designing FIR digital filters demonstrate that IGQA is better than GQA and other GAs because IGQA has faster convergent speed and better global search capability, and IGQA can overcome premature phenomenon effectively. The performances of filters designed by IGQA are better than that of LTUM.
出处
《电讯技术》
北大核心
2003年第5期41-46,共6页
Telecommunication Engineering
基金
国家自然科学基金资助项目(69574026)
教育部骨干教师资助计划项目
