• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Informatics in Control, Automation and Robotics >Optimal design of digital low pass Finite Impulse Response filter using Particle Swarm Optimization and Bat Algorithm
【24h】

Optimal design of digital low pass Finite Impulse Response filter using Particle Swarm Optimization and Bat Algorithm

机译：基于粒子群优化和Bat算法的数字低通有限冲激响应滤波器的优化设计。

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, the traditional metaheuristic Particle Swarm Optmization (PSO) and the Bat Algorithm (BA) are used to optimal design digital low pass (LP) Finite Impulse Response (FIR) filters. These filters have a wide range of applications because of their characteristics. They are easy to be designed, they have guaranteed bounded input-bounded output (BIBO) stability and can be designed to present linear phase at all frequencies. Traditional optimization methods based on gradient are susceptible to getting trapped on a local optima solution when they are applied to optimize multimodal problems, such as the FIR filter design. Here, to overcome this drawback, the aforementioned metaheuristics are adopted to obtain the coefficients of low pass FIR filters of order 20 and 24. The performance of BA and PSO algorithms are compared with the classical Parks and McClellan (PM) filter design algorithm, which is a deterministic procedure. For this comparison is considered the filters pass band and stop band ripples, transition width and statistical data. The simulation results demonstrate that the proposed filter design approach using BA algorithm outperforms PM and PSO.
机译：在本文中，传统的元启发式粒子群优化算法（PSO）和Bat算法（BA）用于优化设计数字低通（LP）有限脉冲响应（FIR）滤波器。这些过滤器由于其特性而具有广泛的应用范围。它们易于设计，保证了有界输入有界输出（BIBO）的稳定性，并且可以设计成在所有频率下呈现线性相位。将基于梯度的传统优化方法应用于FIR滤波器设计等多模态问题时，很容易陷入局部最优解。在这里，为了克服此缺点，采用上述的元启发法来获得20和24阶的低通FIR滤波器的系数。将BA和PSO算法的性能与经典的Parks和McClellan（PM）滤波器设计算法进行比较，是确定性的过程。为了进行这种比较，考虑了滤波器的通带和阻带波纹，过渡宽度和统计数据。仿真结果表明，所提出的采用BA算法的滤波器设计方法优于PM和PSO。

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