自然启发烟花算法在非线性有源噪声控制系统中的应用

发布时间：2024-02-22 19:04

　　在本文中,基于全局优化的烟花算法(FWA),将现代计算启发式范式应用于非线性有源噪声控制系统(ANC)。参考麦克风用于采集噪声信号,误差麦克风用于采集残差噪声信号,该信号同时也被控制算法使用。烟花算法是2010年提出的专门针对集群智能的算法,其最初灵感来源于烟花爆炸的概念。基于ANC系统,展现了烟花算法及其变体在系统识别领域的影响。使用进化和计算启发式范式算法设计基于ANC的控制器。该控制器用全局烟花算法搜索优化的非线性Volterra滤波来表示。自然启发烟花算法用于更新自适应Volterra滤波器ANC系统的参数,而无需识别次路模型。提出的均方误差优化概念用于最小化成本函数。将主/次路视为线性/非线性时,基于烟花算法ANC系统具有正弦噪声信号,随机噪声信号以及复杂随机噪声信号。基于统计的观察结果,通过准确性,复杂性以及收敛性分析证明了随机求解器FWA的价值,即所提出的基于ANC控制器的优化机制是有效,鲁棒且稳定的。此外,本文将所提出的算法与回溯搜索算法(BSA)和粒子群优化算法(PSO)进行了比较。针对具有精度,鲁棒性和稳定性的非线性ANC系统,开发了新颖的自然启发烟花算法。推荐计算...

【文章页数】：75 页

【学位级别】：硕士

【文章目录】：
Dedication
摘要
ABSTRACT
Chapter 1 Introduction
    1.1 Incitement and Motivation
    1.2 Literature Study
    1.3 Endowment and Contribution
    1.4 Background Study of ANC System
    1.5 Declaration of the Problem
    1.6 System Design for the Study
    1.7 Thesis Organization
Chapter 2 Background Studies
    2.1 Noise Cancellation/Noise Reduction
    2.2 Passive Noise Control (PNC) System
    2.3 Active Noise Control (ANC) System
    2.4 Types of ANC System
        2.4.1 Feed-forward ANC System
        2.4.2 Feed-back ANC System
    2.5 Adaptive Controller
        2.5.1 The Adaptive Filters
    2.6 Adaptive algorithms for ANC Systems
        2.6.1 LMS (Least Mean Square Algorithm)
        2.6.2 NLMS Algorithm
        2.6.3 Filter-Extended LMS Algorithm
    2.7 Linear System
    2.8 Non-Linear System
    2.9 Nature-Inspired Algorithm
    2.10 Algorithms classification
        2.10.1 Swarm intelligence based
        2.10.2 Nature-inspired and not SI based
    2.11 Physical Design of Active Noise Control System
    2.12 Applications and Limitations
    2.13 Conclusion
Chapter 3 Firework Algorithm for Nonlinear ANC System
    3.1 Sources of Motivation
    3.2 Nature Inspired Algorithm for ANC System
    3.3 Firework Algorithm for ANC System
    3.4 Points of Contribution
    3.5 Proposed ANC System with FWA Algorithm
    3.6 Proposed Methodology
        3.6.1 Fitness Function Formulation
        3.6.2 Optimization Procedure
    3.7 Conclusion
Chapter 4 Results and Discussion
    4.1 Programming Environment
        4.1.1 System Architecture
    4.2 Simulation Results
        4.2.1 Problem 1: ANC system with the sinusoidal signal
        4.2.2 Problem 2: ANC system with the random signal
        4.2.3 Problem 3: ANC system with Complex random signal
    4.3 Comparative Study
    4.4 Conclusion
Chapter 5 Conclusion and Future Work
    5.1 Conclusion
    5.2 Future Work
Bibliography
Acknowledgments
Publications and Submissions



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