基于PSO的FIR数字滤波器设计

有限冲激响应(FIR)数字滤波器的设计实质是一个多参数优化的问题,而传统的一些优化设计方法,如遗传算法、神经网络法等,存在算法复杂,收敛速度慢,效果不明显等缺点.提出一种改进粒子群优化算法(IMPSO)的FIR数字滤波器设计.该方法首先根据粒子聚合度情况引入变异思想,克服PSO算法容易早熟的毛病,对算法进行改进,然后利用改进的IMPSO搜索滤波器参数的最优解,对FIR滤波器进行优化设计.实例设计FIR数字低通、带通滤波器,仿真结果表明,该方法具有算法简单,收敛速度快,鲁棒性好等优点.     

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。     

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。     

基于改进遗传量子算法的FIR数字滤波器设计

采用改进遗传量子算法(IGQA)进行FIR数字滤波器的优化设计,将滤波器的过渡带样本值作为变量进行优化,解决了传统方法(查表法)不能保证数据最优的问题。针对遗传量子算法(GQA)在优化连续多峰函数时易出现早熟的问题,提出一种改进遗传量子算法(IGQA),典型函数测试表明,IGQA的性能优于GQA和其它几种遗传算法,收敛速度快,全局寻优能力强,能有效地克服早熟现象。采用IGQA优化设计的FIR数字低通和带通滤波器的性能较查表法得到了很大改善。     

IIR数字滤波器的粒子群优化设计方法

探讨了IIR（Infinite Impulse Response）数字滤波器的设计,重点研究了IIR数字滤波器设计的粒子群优化（Particle Swarm Optimization,PSO）算法及算法框图,总结了粒子群优化算法的实现步骤。这一算法的提出,避免了传统算法的缺点,所设计的滤波器达到了理想的性能。通过实验表明,IIR数字滤波器的粒子群优化算法优于遗传算法。     

多带FIR数字滤波器的频域设计

介绍FIR滤波器的FFT快速算法实现方法,推导了FIR数字滤波器的频域直接计算H(k)的计算公式,并进行分析与讨论.根据实际应用需要的滤波器的技术指标,以一个多带FIR线性相位数字滤波器为例进行设计.详细讨论了过渡点的优化设计方法,给出了边界频率点幅度搜索算法,通过用Matlab进行设计和性能分析,结果表明优化后的滤波器能够满足指标要求.     

遗传算法在FIR滤波器设计——频率抽样法中的应用

本文介绍了遗传算法在FIR滤波器设计——频率抽样法中的应用.用遗传算法确定过渡带样本值,解决了传统方法(查表法)不能保证数据是最优的问题.本文还对标准遗传算法进行了适当的改进.给出了FIR数字低通、带通滤波器设计的两个例子.实验结果说明通过遗传算法设计的FIR滤波器性能较查表法得到了改善.     

FPGA实现FIR抽取滤波器的设计

采用基于分布式算法思想的方法来设计FIR滤波器,利用FDAtool设计系统参数,计算滤波器系数,同时为了要满足系统要求考虑系数的位数。根据FIR数字滤波器结构,对FIR数字滤波器的FPGA实现方法进行分析。     

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。     

FIR数字滤波器的设计及其在MATLAB中的仿真实现

介绍了FIR数字滤波器的设计方法,以及MATLAB工具箱中交互式信号处理工具--SPTool在滤波器设计中的应用.并以两个FIR数字带通滤波器(中心频率分别是90Hz和150Hz,带宽都是30Hz)的设计为例,详细说明了采用Least Squares FIR准则、利用SPTool工具设计FIR的步骤.所设计的滤波器通带内波纹小于0.2dB,阻带衰减大于40dB.     

Design of two-dimensional IIR digital filters by using a novel hybrid optimization algorithm

This paper proposes a hybrid optimization algorithm named as BBO–PSO, which is a combination of biogeography-based optimization (BBO) and particle swarm optimization (PSO). In BBO–PSO, the whole population will be split into several subgroups and BBO is employed for local search in each subgroup independently to achieve the different local optima while PSO is employed for global search based on the local optima to achieve the global optimum. The test results on the benchmark functions show that BBO–PSO has powerful search ability with great robustness. Furthermore, the proposed algorithm is applied to the design of the 2-D IIR digital filters and the simulation results show that it outperforms the existing methods on this problem.     

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。     

Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

This article studies the performance of two metaheuristics, particle swarm optimization (PSO) and genetic algorithms (GA), for FIR filter design. The two approaches aim to find a solution to a given objective function but employ different strategies and computational effort to do so. PSO is a more recent heuristic search method than GA; its dynamics exploit the collaborative behavior of biological populations. Some researchers advocate the superiority of PSO over GA and highlight its capacity to solve complex problems thanks to its ease of implementation. In this paper, different versions of PSOs and GAs including our specific GA scheme are compared for FIR filter design. PSO generally outperforms standard GAs in some performance criteria, but our adaptive genetic algorithm is shown to be better on all criteria except CPU runtime. The study also underlines the importance of introducing intelligence in metaheuristics to make them more efficient by embedding self-tuning strategies. Furthermore, it establishes the potential complementarity of the approaches when solving this optimization problem.     

基于频率采样技术的FIR数字滤波器优化设计

本文提出了一种FIR数字滤波器满意优化设计方法，将滤波器过渡带样本值作为优化变量，通过设计通带、过渡带和阻带性能指标满意度函数和综合满意度函数，构造出满意优化模型，并用本文提出的新量子遗传算法搜索满意解，FIR高通和带阻数字滤波器的设计结果表明，采用满意优化方法设计的FIR滤波器的性能优于传统方法。     

Optimal Design of 2D FIR Filters with Quadrantally Symmetric Properties Using Fractional Derivative Constraints

In this article, an optimal design of two-dimensional finite impulse response (2D FIR) filter with quadrantally even symmetric impulse response using fractional derivative constraints (FDCs) is presented. Firstly, design problem of 2D FIR filter is formulated as an optimization problem. Then, FDCs are imposed over the integral absolute error for designing of the quadrantally even symmetric impulse response filter. The optimized FDCs are applied over the prescribed frequency points. Next, the optimized filter impulse response coefficients are computed using a hybrid optimization technique, called hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA). Further, FDC values are also optimized such that flat passband and stopband frequency response is achieved and the absolute \(L_1\)-error is minimized. Finally, four design examples of 2D low-pass, high-pass, band-pass and band-stop filters are demonstrated to justify the design accuracy in terms of passband error, stopband error, maximum passband ripple, minimum stopband attenuation and execution time. Simulation results have been compared with the other optimization algorithms, such as real-coded genetic algorithm, particle swarm optimization and gravitational search algorithm. It is observed that HPSO-GSA gives improved results for 2D FIR-FDC filter design problem. In comparison with other existing techniques of 2D FIR filter design, the proposed method shows improved design accuracy and flexibility with varying values of FDCs.     

Design and performance of adaptive systems based on structured stochastic optimization strategies

The theory and design of linear adaptive filters based on FIR filter structures is well developed and widely applied in practice. However, the same is not true for more general classes of adaptive systems such as linear infinite impulse response adaptive filters (MR) and nonlinear adaptive systems. This situation results because both linear IIR structures and nonlinear structures tend to produce multi-modal error surfaces for which stochastic gradient optimization strategies may fail to reach the global minimum. After briefly discussing the state of the art in linear adaptive filtering, the attention of this paper is turned to MR and nonlinear adaptive systems for potential use in echo cancellation, channel equalization, acoustic channel modeling, nonlinear prediction, and nonlinear system identification. Structured stochastic optimization algorithms that are effective on multimodal error surfaces are then introduced, with particular attention to the particle swarm optimization (PSO) technique. The PSO algorithm is demonstrated on some representative IIR and nonlinear filter structures, and both performance and computational complexity are analyzed for these types of nonlinear systems.     

基于改进PSO的非对称BoucWen模型参数辨识

为了辨识压电陶瓷中的迟滞非线性,该文提出一种改进的粒子群算法(PSO)对非对称BoucWen模型进行参数优化。首先在归一化BoucWen模型中引入非对称因子描述非对称特性,解决该模型只适用于描述对称迟滞的问题。其次通过引入混沌映射、收缩因子和动态学习因子来对传统PSO进行改进,动态改变粒子群的权重和学习因子,有效地提高算法的搜索能力和收敛速度。最后通过改进的PSO对非对称BoucWen模型进行参数辨识。结果表明,改进的粒子群算法能较好地辨识BoucWen模型参数,验证了方法的有效性。