粒子群优化
滤波器设计
有限冲激响应
算法
滤波器(信号处理)
自适应滤波器
低通滤波器
核自适应滤波器
计算机科学
数学优化
数学
作者
Sandeep Singh,Gagan Singh,Sourav Bose,None Shiva
出处
期刊:Lecture notes in electrical engineering
日期:2022-07-28
卷期号:: 249-257
标识
DOI:10.1007/978-981-19-2468-2_28
摘要
AbstractIn this paper, digital finite impulse response (FIR) low-pass filter (LPF) and high-pass filter (HPF) are designed using a novel meta-heuristic algorithm named grasshopper optimization algorithm (GOA). The GOA is meta-heuristic population-based optimization algorithm, which mimics the food searching behaviour of the grasshopper. The filter design aims to evaluate the optimal filter parameters and find the minimum objective function value so that the output of the designed filter matches with the output response of the ideal filter. Mean square error (MSE) is taken as the error objective function. The results obtained using GOA are compared with the other two algorithms, namely particle swarm optimization (PSO) algorithm and grey wolf optimization (GWO) algorithm. The simulated results reveal that GOA is best suited algorithm for FIR filter design problem.KeywordsFIR filter designMean Square ErrorParticle Swarm Optimization AlgorithmGrey Wolf Optimization AlgorithmGrasshopper Optimization Algorithm
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