【摘要】：線性自適應(yīng)濾波算法解決一些問(wèn)題時(shí)有一定優(yōu)越性,但是,實(shí)際中所研究的問(wèn)題涉及的往往是一些非線性問(wèn)題。線性算法在處理非線性問(wèn)題時(shí),表現(xiàn)出的性能效果并不令人滿意,例如,在噪聲對(duì)消及信道均衡等應(yīng)用場(chǎng)合。核方法(Kernel Methods)隨著支持向量機(jī)的研究而被引入到非線性領(lǐng)域。核方法為研究非線性問(wèn)題提供了一種技術(shù)路徑,通過(guò)在合適的高維特征空間中用線性算法來(lái)處理原空間中的非線性關(guān)系。核方法已廣泛應(yīng)用在模式識(shí)別和圖像處理等問(wèn)題中。當(dāng)前,存在眾多的線性自適應(yīng)濾波算法,如何利用這些已有成果,借助核方法研究出更多的、有效的非線性濾波算法具有重要的理論意義和價(jià)值。核方法是一種由線性算法誘導(dǎo)出非線性算法的有效技術(shù)。針對(duì)最小均方(LMS)算法在處理非線性問(wèn)題時(shí)性能不理想的缺陷,本文利用模式識(shí)別中的核方法思想研究了兩種新穎的非線性自適應(yīng)濾波算法,分別是歸一化最小均方(NLMS)算法和四階誤差信號(hào)最小化(LMF)算法的非線性版本,稱為KNLMS算法和KLMF算法。本文的主要研究工作總結(jié)如下:(1)利用核方法原理,研究了兩種核自適應(yīng)濾波算法:核歸一化最小均方(KNLMS)算法及核四階誤差信號(hào)最小化(KLMF)算法。提供了這兩種算法的數(shù)值實(shí)驗(yàn)結(jié)果。(2)討論了所研究KLMF算法在噪聲對(duì)消中的應(yīng)用。對(duì)比仿真實(shí)驗(yàn)說(shuō)明了算法的可用性與有效性。(3)討論了所研究的KLMF算法在非線性信道均衡中的應(yīng)用。對(duì)比仿真實(shí)驗(yàn)結(jié)果說(shuō)明了算法的可用性與優(yōu)勢(shì),同時(shí)分析了不同參數(shù)對(duì)算法性能的影響。
[Abstract]:The linear adaptive filtering algorithm has some advantages in solving some problems, but the problems studied in practice are often related to some nonlinear problems. The performance of the linear algorithm in dealing with nonlinear problems is not satisfactory, for example, in the applications of noise cancellation and channel equalization. The kernel method (Kernel Methods) is introduced into the nonlinear field with the research of support vector machine (SVM). The kernel method provides a technical path for the study of nonlinear problems. The linear algorithm is used to deal with the nonlinear relations in the original space in a suitable high dimensional feature space. Kernel method has been widely used in pattern recognition and image processing. At present, there are many linear adaptive filtering algorithms. It is of great theoretical significance and value to study more and more effective nonlinear filtering algorithms with the help of kernel method. Kernel method is an effective technique to induce nonlinear algorithm by linear algorithm. Aiming at the shortcoming of the least mean square (LMS) algorithm in dealing with nonlinear problems, two novel nonlinear adaptive filtering algorithms are studied by using the kernel method in pattern recognition. The nonlinear versions of normalized minimum mean square (NLMS) algorithm and fourth-order error signal minimization (LMF) algorithm are called KNLMS algorithm and KLMF algorithm respectively. The main work of this paper is summarized as follows: (1) two kernel adaptive filtering algorithms, kernel normalized least mean square (KNLMS) algorithm and kernel fourth-order error signal minimization (KLMF) algorithm, are studied by using the kernel method principle. Numerical results of the two algorithms are presented. (2) the application of the proposed KLMF algorithm in noise cancellation is discussed. The simulation results show the availability and effectiveness of the proposed algorithm. (3) the application of the proposed KLMF algorithm in nonlinear channel equalization is discussed. The simulation results show the availability and advantage of the algorithm, and analyze the influence of different parameters on the performance of the algorithm.
【學(xué)位授予單位】：西華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN713;TN911.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 宋紫毓;彭盛亮;趙睿;;可見(jiàn)光通信中一種基于分段誤差函數(shù)的變步長(zhǎng)LMS算法[J];信號(hào)處理;2017年04期

2 馬令坤;吳波;毛紅艷;;改進(jìn)的變誤差寬度變階數(shù)LMS算法[J];現(xiàn)代電子技術(shù);2017年01期

3 劉牮;李_g;;一種用于信號(hào)估計(jì)的改進(jìn)變步長(zhǎng)LMS算法[J];電力科學(xué)與工程;2016年10期

4 李亮;;基于再生核Hilbert空間的非線性信道均衡算法[J];計(jì)算機(jī)工程與應(yīng)用;2016年16期

5 陳緒君;朱宇芳;胡君紅;馬得宇;;滑動(dòng)窗近似線性依賴稀疏的核遞推最小二乘算法[J];計(jì)算機(jī)工程;2016年08期

6 管四海;李智;;基于修正反正切函數(shù)的變步長(zhǎng)LMS算法[J];中國(guó)科技論文;2016年14期

7 李麗;石翠萍;李會(huì);;VoIP回聲消除中LMS改進(jìn)算法的研究[J];高師理科學(xué)刊;2016年06期

8 王蒙;趙建平;張炳婷;;一種基于雙曲正割函數(shù)的變步長(zhǎng)LMS算法[J];通信技術(shù);2016年06期

9 王洪誠(chéng);王蕾;沈霞;楊欣榮;王正;;基于改進(jìn)增益型自適應(yīng)LMS算法的諧波檢測(cè)方法[J];電力系統(tǒng)保護(hù)與控制;2016年05期

10 林云;雷洋;曾俊俊;;抗沖擊噪聲的核對(duì)數(shù)最小絕對(duì)差算法[J];電子技術(shù)應(yīng)用;2016年02期

相關(guān)碩士學(xué)位論文 前3條

1 趙一逍;基于核函數(shù)的自適應(yīng)濾波算法研究及其系統(tǒng)實(shí)現(xiàn)[D];北京理工大學(xué);2015年

2 陳乾;核自適應(yīng)濾波算法研究[D];華中師范大學(xué);2014年

3 苗秋園;核自適應(yīng)濾波算法的研究[D];浙江大學(xué);2012年

，

本文編號(hào)：2317378