在本文中,基于全局優(yōu)化的煙花算法(FWA),將現(xiàn)代計(jì)算啟發(fā)式范式應(yīng)用于非線性有源噪聲控制系統(tǒng)(ANC)。參考麥克風(fēng)用于采集噪聲信號(hào),誤差麥克風(fēng)用于采集殘差噪聲信號(hào),該信號(hào)同時(shí)也被控制算法使用。煙花算法是2010年提出的專門針對(duì)集群智能的算法,其最初靈感來源于煙花爆炸的概念�；贏NC系統(tǒng),展現(xiàn)了煙花算法及其變體在系統(tǒng)識(shí)別領(lǐng)域的影響。使用進(jìn)化和計(jì)算啟發(fā)式范式算法設(shè)計(jì)基于ANC的控制器。該控制器用全局煙花算法搜索優(yōu)化的非線性Volterra濾波來表示。自然啟發(fā)煙花算法用于更新自適應(yīng)Volterra濾波器ANC系統(tǒng)的參數(shù),而無需識(shí)別次路模型。提出的均方誤差優(yōu)化概念用于最小化成本函數(shù)。將主/次路視為線性/非線性時(shí),基于煙花算法ANC系統(tǒng)具有正弦噪聲信號(hào),隨機(jī)噪聲信號(hào)以及復(fù)雜隨機(jī)噪聲信號(hào)�；诮y(tǒng)計(jì)的觀察結(jié)果,通過準(zhǔn)確性,復(fù)雜性以及收斂性分析證明了隨機(jī)求解器FWA的價(jià)值,即所提出的基于ANC控制器的優(yōu)化機(jī)制是有效,魯棒且穩(wěn)定的。此外,本文將所提出的算法與回溯搜索算法(BSA)和粒子群優(yōu)化算法(PSO)進(jìn)行了比較。針對(duì)具有精度,魯棒性和穩(wěn)定性的非線性ANC系統(tǒng),開發(fā)了新穎的自然啟發(fā)煙花算法。推薦計(jì)算...

【文章頁(yè)數(shù)】：75 頁(yè)

【學(xué)位級(jí)別】：碩士

【文章目錄】：
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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