Nonlinear Active Noise Control system using Nature-Inspired Algorithm for Coefficients updates of Adaptive Volterra filter

被引：0
|
作者：
Khan, Muhammad Alamgeer [1 ]
Khan, Muhammad Aurang Zeb [1 ]
Sohail, Muhammad [1 ]
Lei, Wang [1 ]
机构：
[1] Univ Sci & Technol China, Dept Automat, Hefei 230026, Anhui, Peoples R China
来源：
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年
关键词：
Active noise control (ANC); Firework algorithm (FWA); Mean squared error (MSE); System identification; Backtracking Search Algorithm (BSA); Particle Swarm Optimization (PSO); FIREWORKS ALGORITHM; OPTIMIZATION; CANCELLATION; SENSOR;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, natured inspired firework algorithm is used to update the coefficients of adaptive Volterra filter based ANC systems without identification of secondary path modeling. The concept of mean squared error sense is used to develop the cost function. Firework algorithm based ANC systems having sinusoidal noise signal, random noise signal and complex random noise signal while taking primary and secondary paths as linear/nonlinear. Observations based on Statistics validated the worth of stochastic solvers FWA by means of accuracy, complexity analysis and convergence. The results of the proposed algorithm is compared with the backtracking search algorithm (BSA) and particle swarm optimization (PSO).
引用
收藏
页码：1224 / 1230
页数：7
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