Optimization of IIR high pass filter using craziness based particle swarm optimization technique

S. Saha, Annesha Chaudhuri, D. Mandal, R. Kar, S. Ghoshal
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引用次数: 7

Abstract

In this paper, a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) is used for the design of 8th order infinite impulse response (IIR) digital filter. The proposed optimization technique is a global heuristic search algorithm and better exploration and exploitation of multidimensional search space can be achieved with closely mimicked swarm behaviour in fundamental PSO equation. Performance of the proposed optimization technique is compared with some well accepted evolutionary algorithms such as PSO and real coded genetic algorithm (RGA). From the simulation study it is established that the CRPSO outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality, i.e., the stop band attenuation, transition width, pass band and stop band ripples. Further, the pole-zero analysis justifies the stability of the designed optimized IIR filter.
基于疯狂度的粒子群优化技术优化IIR高通滤波器
本文将粒子群算法的一种变体——基于疯狂度的粒子群算法(CRPSO)应用于八阶无限脉冲响应数字滤波器的设计。所提出的优化技术是一种全局启发式搜索算法,通过近似模拟基本粒子群优化方程中的群体行为,可以更好地探索和利用多维搜索空间。将所提优化技术的性能与一些公认的进化算法(如粒子群优化算法和实编码遗传算法)进行了比较。仿真研究表明,CRPSO不仅在设计滤波器的精度上优于RGA和PSO,而且在收敛速度和解的质量上也优于RGA和PSO,即阻带衰减、过渡宽度、通带和阻带波纹。此外,极点-零点分析验证了优化后的IIR滤波器的稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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