隨著工業(yè)化和城市化的快速發(fā)展,嚴(yán)重的噪聲污染的出現(xiàn),誕生了一個(gè)有趣的噪聲控制研究領(lǐng)域。從技術(shù)上講,噪聲控制領(lǐng)域大致可以分為兩種方式:被動(dòng)和主動(dòng)。被動(dòng)噪聲控制(PNC)技術(shù)使用特殊材料來(lái)吸收或/和隔離不需要的噪聲。然而這些材料設(shè)備通常體積大,安裝困難,成本高,對(duì)低頻噪聲下消噪效果低差。相比之下,主動(dòng)噪聲控制(ANC)技術(shù)可以克服PNC方法的缺點(diǎn),能有效消除或降低低頻噪聲。隨著電子技術(shù)和自適應(yīng)處理理論的發(fā)展,由于在重量、尺度和成本等方面都有潛在的優(yōu)勢(shì),ANC噪聲控制技術(shù)得到了越來(lái)越多的重視。本文主要研究在系統(tǒng)非線性較強(qiáng)的情況下,采用FLANN對(duì)ANC系統(tǒng)進(jìn)行新的非線性自適應(yīng)控制器設(shè)計(jì),以提高噪聲消除性能,降低計(jì)算復(fù)雜度。主要包括以下幾個(gè)方面:首先,研究基于FLANN的ANC系統(tǒng)的性能,包括分析ANC系統(tǒng)中非線性的特性,分析FLANN對(duì)ANC系統(tǒng)的非線性建模能力;其次,基于這些分析,提出多種含有交叉項(xiàng)的FLANN控制器結(jié)構(gòu)。此外,基于濾波誤差技術(shù)和數(shù)據(jù)相關(guān)的部分更新策略設(shè)計(jì)新的算法,從而進(jìn)一步減少計(jì)算負(fù)擔(dān)。本論文的主要貢獻(xiàn)如下:(1)基于對(duì)ANC系統(tǒng)組件中的非線性特性的分析,討論了在實(shí)際AN...

【文章頁(yè)數(shù)】：178 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
摘要
abstract
List of Abbreviations
Chapter1 Introduction
    1.1 Significance and background of the research
    1.2 Overview of the ANC system
        1.2.1 Basic principle
        1.2.2 Development of ANC system
    1.3 The research situation
    1.4 Motivation
    1.5 The main research results of the dissertation
    1.6 Organization of the Dissertation
Chapter2 Analysis of nonlinear influence and FLANN model in ANC system
    2.1 Introduction
    2.2 Analysis of nonlinear influence on the ANC system
        2.2.1 Nonlinear influence in the reference noise
        2.2.2 Nonlinear influence in the primary path
        2.2.3 Nonlinear influence in the secondary path
    2.3 Type of nonlinearity in ANC systems
        2.3.1 Memory and Memoryless nonlinearity
        2.3.2 Chaotic nonlinearity
    2.4 Functional link artificial neural networks model
        2.4.1 Structure
        2.4.2 Nonlinear adaptive FLANN filter
    2.5 The ANC system based on FLANN
        2.5.1 Structure
        2.5.2 The Filtered-S least mean square(FsLMS)algorithm
    2.6 Analysis of the nonlinear modeling capability of FLANN for the ANC system
    2.7 The performance evaluation of the FLANN-based ANC system
        2.7.1 Evaluation of noise-canceling performance
        2.7.2 Evaluation of computational resources performance
    2.8 Conclusion
Chapter3 Simplified generalized FLANN filter for nonlinear active noise control
    3.1 Introdution
    3.2 Filter bank implementation of a class of nonlinear filters
    3.3 The nonlinear adaptive simplified generalized FLANN(SG-FLANN)controller for ANC system
        3.3.1 The generalized FLANN nonlinear filter with simplified diagonal-structure
        3.3.2 The simplified generalized Fs-LMS(SGFs-LMS)algorithm
        3.3.3 M-max simplified generalized filtered error least mean square (Mm SGFE-LMS) algorithm
    3.4 The analysis of adaptive SG-FLANN filter in nonlinear ANC systems
    3.5 Stability conditions of adaptive algorithms
    3.6 Computational complexity analysis
        3.6.1 Computational Complexity for NANC/LSP
        3.6.2 Computational Complexity for NANC/NSP
    3.7 Simulation
        3.7.1 Experiment 3.1
        3.7.2 Experiment 3.2
    3.8 Conclusion
Chapter4 Nonlinear adaptive bilinear FLANN filter for active noise control
    4.1 Introduction
    4.2 The nonlinear adaptive bilinear filter
    4.3 The nonlinear adaptive bilinear FLANN filter for ANC
        4.3.1 The bilinear FLANN structure
        4.3.2 Leaky bilinear filter x-least mean square(LBFx-LMS)algorithm
        4.3.3 M-max partial update Leaky bilinear filter-error least mean square (Mm LBFE-LMS) algorithm
    4.4 The bounded-input bounded-output(BIBO)stability condition of bilinear FLANN
    4.5 Computational complexity analysis
        4.5.1 Computational Complexity for NANC/LSP
        4.5.2 Computational Complexity for NANC/NSP
    4.6 Simulation
        4.6.1 The nonlinear ANC with nonlinear secondary path
        4.6.2 The nonlinear ANC with linear secondary path
    4.7 Conclusion
Chapter5 Generalized exponential FLANN filter with channel-reduced diagonal structure for nonlinear active noise control
    5.1.Introduction
    5.2.Nonlinear adaptive exponential FLANN filter
    5.3.The generalized E-FLANN with channel-reduced diagonal(GE-FLANN-CRD)filter for ANC
        5.3.1.The generalized E-FLANN filter and its multichannel implementation
        5.3.2.Generalized exponential filtered-s least mean square(GEFs-LMS)algorithm
        5.3.3 M-max generalized exponential filtered-error least mean square(MmGEFE-LMS)algorithm
    5.4 Convergence analysis and stability conditions
    5.5 Computational complexity analysis
    5.6 Simulation
        5.6.1 Experiment5.1
        5.6.2 Experiment5.2
    5.7 Conclusion
Chapter6 Computationally efficient pipelined architecture-based adaptive generalized FLANN filter for nonlinear active noise control
    6.1.Introduction
    6.2 Nonlinear adaptive pipelined generalized FLANN(P-GFLANN)filter
        6.2.1 P-GFLANN structure
        6.2.2 Adaptive algorithm of the P-GFLANN filter
        6.2.3 Stability conditions analysis
        6.2.4 Computational complexity analysis
        6.2.5 The performance evaluation of the P-GFLANN
    6.3 The nonlinear adaptive P-GFLANN filter for ANC
        6.3.1 Structure of the ANC system based on P-GFLANN
        6.3.2 Pipelined generalized filtered-s least mean squre(PGFs-LMS)algorithm
    6.4 Nonlinear adaptive hierarchical update P-GFLANN (HUP-GFLANN) filter for ANC
        6.4.1 Structure of the NANC system based on the HUP-GFLANN filter
        6.4.2 Hierarchical M-max generalized filtered-error least mean square (HMm GFE-LMS) algorithm
    6.5 Computational complexity analysis for the pipelined architecture-based ANC system
    6.6 Simulation
        6.6.1 Experiment6.1
        6.6.2 Expriment6.2
        6.6.3 Experiment6.3
        6.6.4 Experiment6.4
        6.6.5 Experiment6.5
    6.7 Conclusion
Conclusion and Future Work
Conclusions
Recommendations for Future Work
Acknowledgements
Reference
Appendix
List of Publication



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