...
首页> 外文期刊>AEU: Archiv fur Elektronik und Ubertragungstechnik: Electronic and Communication >Adjustable window based design of multiplier-less cosine modulated filter bank using swarm optimization algorithms
【24h】

Adjustable window based design of multiplier-less cosine modulated filter bank using swarm optimization algorithms

机译:基于群体优化算法的无乘子余弦调制滤波器组可调窗口设计

获取原文
获取原文并翻译 | 示例
           

摘要

In this paper, an adjustable window based approach is presented for the design of multiplier-less near perfect reconstruction cosine modulated filter bank (CMFB) for specified stopband attenuation (A(s)) and channel overlapping. Kaiser window function is employed for designing the computationally efficient prototype filter with filter coefficients in canonic signed digit (CSD) space. Optimized performance of the designed filter is achieved using swarm based algorithm such as cuckoo search (CS) optimization, so that the filter coefficients of a multiplier-less prototype filter are optimized to achieve the magnitude response of 0.707 at frequency omega=pi/2M. In this method, instead of using two optimization techniques: one for designing continuous coefficients and other for optimizing quantized prototype filter coefficients, single swarm based technique is used, while a comparative study using proposed scheme based on the performance of different window functions as well as different swarm based techniques such particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm is made. Design examples presented, using this technique, illustrates the improved performance of proposed technique as compared to other published algorithms. (C) 2015 Elsevier GmbH. All rights reserved.
机译:在本文中,提出了一种基于可调整窗口的方法,用于针对指定阻带衰减(A(s))和通道重叠设计无乘法器的接近完美重构余弦调制滤波器组(CMFB)。 Kaiser窗函数用于设计具有有效符号原型(CSD)空间中滤波器系数的计算有效原型滤波器。使用基于群的算法(例如布谷鸟搜索(CS)优化)可实现设计滤波器的优化性能,从而优化无乘法器原型滤波器的滤波器系数,以在频率ω= pi / 2M时获得0.707的幅值响应。在这种方法中,不是使用两种优化技术:一种是设计连续系数,另一种是优化量化的原型滤波器系数,而是使用基于单一群的技术,而使用基于不同窗口函数性能的建议方案进行的比较研究以及提出了基于粒子群的不同技术,例如粒子群优化(PSO)和人工蜂群(ABC)算法。提出的使用该技术的设计示例说明了与其他已发布算法相比所提出技术的改进性能。 (C)2015 Elsevier GmbH。版权所有。

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