首页> 外文期刊>Engineering Applications of Artificial Intelligence >Design of frequency response masking FIR filter in the Canonic Signed Digit space using modified Artificial Bee Colony algorithm
【24h】

Design of frequency response masking FIR filter in the Canonic Signed Digit space using modified Artificial Bee Colony algorithm

机译:用改进的人工蜂群算法设计正则符号空间中的频响掩蔽FIR滤波器

获取原文
获取原文并翻译 | 示例
           

摘要

Frequency response masking (FRM) technique along with the Canonic Signed Digit (CSD) representation is a good alternative for the design of a computationally efficient, sharp transition width, high speed finite impulse response (FIR) filter. This paper proposes two novel approaches for the joint optimization of an FRM FIR digital filter in the CSD space. The first approach uses the recently emerged Artificial Bee Colony (ABC) algorithm and the second approach uses the Differential Evolution (DE) algorithm. In this paper, both the algorithms are modified in such a way that, they are suitable for the solution of the optimization problem posed, in which the search space consists of integers and the objective function is nonlinear. The optimization variables are encoded such that they permit the reduction in computational cost. The salient feature of the above approaches is the reduced computational complexity while obtaining good performance. Simulation results show that the ABC based design technique performs better than that using DE, which in turn outperforms the one using integer coded genetic algorithm (GA). The proposed optimization approaches can be extended to the solution of integer programming problems in other engineering disciplines also.
机译:频率响应掩蔽(FRM)技术和Canonic Signed Digit数字(CSD)表示法是设计高效,锐利的过渡宽度,高速有限脉冲响应(FIR)滤波器的设计的理想选择。本文为CSD空间中的FRM FIR数字滤波器的联合优化提出了两种新颖的方法。第一种方法使用最近出现的人工蜂群(ABC)算法,第二种方法使用差分进化(DE)算法。本文对这两种算法进行了修改,使其适合于所提出的优化问题,即搜索空间由整数组成,目标函数为非线性。对优化变量进行编码,以使它们可以降低计算成本。上述方法的显着特征是减少的计算复杂度,同时获得良好的性能。仿真结果表明,基于ABC的设计技术的性能优于使用DE的设计技术,而DE的性能又优于使用整数编码遗传算法(GA)的设计技术。所提出的优化方法也可以扩展到其他工程学科中整数规划问题的解决方案。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号