IIR数字滤波器设计的粒子群优化算法

本文探讨了粒子群优化算法及其性能评估准则，然后重点研究了IIR数字滤波器设计的粒子群优化算法及其实现步骤。最后，通过IIR数字低通、带通滤波器设计两个实例证明了本文算法的有效性。     

基于人工鱼群算法的IIR数字滤波器设计

将人工鱼群算法（AFSA）用于IIR数字滤波器设计，建立了相应的优化模型，给出了简化的人工鱼群算法及其实现步骤。最后，将该算法用于低通、带通IIR数字滤波器的设计，并与粒子群算法进行了比较。仿真结果证明了AFSA的有效性，并且具有算法灵活、简单，全局收敛性好。收敛速度快的优点。     

基于免疫机制的PSO算法中IIR数字滤波器设计

针对微粒群算法(PSO)在搜索过程中粒子的多样性差,易陷入局部最优且收敛速度慢等缺陷,将生物免疫系统中克隆选择机制和独特型免疫网络理论引入到微粒群优化算法中,提出了一种基于免疫机制的PSO优化算法(SOIM)并将其用于IIR数字滤波器的设计.该算法结合了微粒群算法的全局寻优能力和免疫多样性保持机制,改善了微粒群算法摆脱局部极值点的能力,提高了算法的收敛速度.仿真结果表明该算法在多模态搜索空间中具有更好的全局收敛性能和稳定性,是一种有效可行的IIR数字滤波器设计方法.     

基于免疫算法的IIR数字滤波器优化与实现

由于IIR数字滤波器设计实质上是一个非线性高维复杂函数优化问题,文中提出基于具有全局搜索能力强,收敛速度快特点的免疫算法实现IIR数字滤波器优化设计的新方法,给出了IIR滤波器优化设计的数学模型,描述了应用免疫算法优化设计IIR数字滤波器的具体实现步骤。通过低通和高通IIR数字滤波器设计的仿真结果表明方法的有效性和高效性。     

IIR数字滤波器的仿真实现研究

作为改变信号频谱的算法装置,数字滤波器及其设计受到人们广泛地关注,其分为有限长冲激响应（FIR）数字滤波器和无限长冲激响应（IIR）数字滤波器。文中探究具有无限持续时间的冲击响应的IIR数字滤波器系统,并提出如何利用MATLAB软件实现仿真设计。     

粒子群优化算法在FIR数字滤波器设计中的应用

本文针对有限脉冲响应(FIR)数字滤波器的设计实质上是一个多参数优化问题,提出了一种用粒子群优化算法(PSO)设计FIR数字滤波器的方法.首先将滤波器的设计问题转化为滤波器参数的优化问题,然后利用粒子群优化算法对整个参数空间进行高效并行搜索以获得参数的最优化.FIR数字低通、带通滤波器设计实例证明了该方法的有效性和优越性.     

QPSO算法在FIR数字滤波器设计中的应用

FIR数字滤波器的设计实际上是一个多维变量寻优问题,滤波器的设计可转化为滤波器参数优化的问题。文章介绍了粒子群优化算法、量子粒子群优化算法,然后利用算法对参数空间进行搜索以获得参数的最优化,根据预期频率特性的设计要求,通过仿真实验表明设计方法的有效性。     

基于自适应变异粒子群优化算法的自适应滤波器设计

本文提出了设计一种基于自适应变异粒子群优化算法的振动信号的自适应滤波模型，然后重点研究了自适应数字滤波器设计的粒子群优化算法及其实现步骤。该滤波模型在计算机仿真测试中，获得了很高的效率和良好的结果。     

基于ⅡR的煤矿井下电法勘探信号处理分析

文章配合ALTERA公司的Max+plus Ⅱ开发平台,使用VHDL语言设计了两种IIR数字滤波器算法。并分析了两种IIR数字滤波器算法的优缺点,从而提高信号处理速度。     

基于Visual DSP＋＋的无限脉冲响应数字滤波器（IIR）设计

对基于Visual DSP＋＋实现无限脉冲响应数字滤波器（IIR）技术进行研究,分析了无限脉冲响应数字滤波器的原理和算法,给出了IIR数字信号处理在Visual DSP＋＋软环境的实现方法,该方法具有一定的工程应用价值。     

Design of linear-phase IIR filters via impulse-response gramians

A new method for the design of a linear-phase infinite-impulse-response (IIR) filter is presented. It involves designing a finite-impulse-response (FIR) filter satisfying the given frequency response specifications and subsequently obtaining a significantly lower order IIR filter using model reduction based on impulse-response gramians. The general outline of the method and a brief overview of the existing linear-phase FIR filter design and model-reduction techniques are presented. The impulse-response gramian and the model-reduction algorithm used are presented. The method is illustrated by design examples and is compared with other methods for the design of linear-phase IIR filters using equalizers     

Cooperative Coevolutionary Genetic Algorithm for Digital IIR Filter Design

A novel algorithm for digital infinite-impulse response (IIR) filter design is proposed in this paper. The suggested algorithm is a kind of cooperative coevolutionary genetic algorithm. It considers the magnitude response and the phase response simultaneously and also tries to find the lowest filter order. The structure and the coefficients of the digital IIR filter are coded separately, and they evolve coordinately as two different species, i.e., the control species and the coefficient species. The nondominated sorting genetic algorithm-II is used for the control species to guide the algorithms toward three objectives simultaneously. The simulated annealing is used for the coefficient species to keep the diversity. These two strategies make the cooperative coevolutionary process work effectively. Comparisons with another genetic algorithm-based digital IIR filter design method by numerical experiments show that the suggested algorithm is effective and robust in digital IIR filter design     

Reduced order IIR approximation to FIR digital filters

An algorithm for designing an infinite-impulse-response (IIR) stable filter using a finite-impulse-response (FIR) given filter, with the objective of reducing the delay and order, is described. The design is in the time domain using the least-squares-inverse algorithm, which is briefly described. In this method, the numerator of the approximated filter is part of the FIR filter itself and no calculations and minimization are needed to find the numerator coefficients (except finding the FIR roots). An error analysis between the given FIR and approximated IIR filters is provided. This error analysis enables the designer to fix a design parameter, often unnoted, keeping the energies of the approximated and original filters equal. Results and two illustrative examples are presented     

基于自适应遗传算法FIR数字滤波器优化设计

FIR数字滤波器优化设计的目标是对滤波器理想性能的逼近。遗传算法是一种模仿生物进化过程的全局优化概率搜索算法,它提出了一种求解复杂系统优化问题的通用框架,且不依赖于问题的领域和种类,在此将自适应的遗传算法应用于FIR数字滤波器的优化设计,通过评价种群的“早熟度”来自适应调整交叉率和变异率,提高了遗传算法的搜索效率。计算机仿真结果证明,该算法能够获得满意的滤波器性能。     

Design of low-order linear-phase IIR filters via orthogonalprojection

This paper presents an indirect linear-phase IIR filter design technique based on a reduction of linear-phase FIR filters. The desired filter is obtained by minimizing the L₂ norm of the difference between the original FIR filter and the lower order IIR filter. We first establish a relationship between the Hankel singular values of the discarded part of the FIR filter and the L₂ norm of the corresponding filter approximation error based on model truncation. This result motivates us to propose a simple finite search method that will achieve better approximation results than commonly used truncation methods such as the balanced truncation (BT) and the impulse response gramian (IRG) methods. We then develop an iterative algorithm for finding an optimal IIR filter based on a matrix projection of the original FIR filter. The convergence of the proposed algorithm is established. Filters designed using the proposed algorithm are compared with those obtained by other techniques with respect to the amplitude response and group delay characteristics in the passband. Numerical examples show that the proposed algorithm offers the best performance     

A Modified Particle Swarm Optimizer and its Application to the Design of Microwave Filters

In this paper, a modified particle swarm optimization (PSO) algorithm is presented and its applicability is shown for the design of specific microwave filter as a case study of microwave components. In the proposed modified PSO algorithm, particles in the swarm are divided to form multiple sub-swarms. The social component of PSO’s velocity update equation is modified to include the effects of multiple sub-swarms. Five benchmark functions have been considered for testing the proposed algorithm. The approach has been tested for two basic modifications of PSO namely PSO with inertia weight (IW) and PSO with constriction factor method (CFM). The simulated results illustrate that the modified PSO algorithm has the potential to converge faster, thus reducing the computational expenses, while maintaining/improving the quality of solution. Finally, the proposed algorithm is used for the design of coupled microstrip line band pass filter which is a computationally expensive process when the design is conducted using evolutionary algorithms and electromagnetic (EM) simulation tools.     

两带自适应FIR线性相位双正交滤波器组设计

自适应滤波器组设计是多速率滤波器组理论和应用的一个重要方面。由于其频率响应更好匹配于输入信号的统计特性，这类滤波器组可获得更大的子带编码增益。该文研究了两带自适应FIR线性相位双正交滤波器组的设计问题，给出了设计算法，特别是通过最优IIR双正交滤波器组确定初始点（初始滤波器组）的方法。仿真结果表明，得到的滤波器组的子带编码增益远远超过了最优的IIR正交滤波器组，与已有的设计结果比较，编码增益明显提高。     

IIR数字滤波器的Matlab和FPGA实现

提出一种通过两个二阶节级联构成四阶IIR数字椭圆滤波器的设计方法,并利用Matlab仿真软件设计了通带内波纹不大于0.1 dB,阻带衰减不小于42 dB的IIR数字滤波器。论述了一种采用可编程逻辑器件,通过VHDL硬件描述语言实现该滤波器的方法。给出了在QuartusⅡ软件下的仿真结果,并在FPGA器件上验证实现。实验证明,这种方法是切实可行的。