本文選題：α穩(wěn)定分布噪聲 + 核遞歸最小平均P范數(shù)��； 參考：《信號(hào)處理》2017年04期

【摘要】：在強(qiáng)脈沖噪聲干擾背景中,核遞歸最小二乘(Kernel Recursive Least Square,KRLS)算法和核遞歸最大相關(guān)熵(Kernel Recursive Maximum Correntropy,KRMC)算法對(duì)非線性信號(hào)預(yù)測(cè)性能嚴(yán)重退化,對(duì)此提出一種核遞歸最小平均P范數(shù)(Kernel Recursive Least Mean P-norm,KRLMP)算法。首先運(yùn)用核方法將輸入數(shù)據(jù)映射到再生核希爾伯特空間(Reproducing Kernnel Hilbert Space,RKHS)。其次基于最小P范數(shù)準(zhǔn)則和正則化方法,推導(dǎo)得到自適應(yīng)濾波器的最佳權(quán)向量,其降低了非高斯脈沖和樣本量少的影響。然后利用矩陣求逆理論,推導(dǎo)得到矩陣的遞歸公式。最后利用核技巧得到在輸入空間高效計(jì)算的濾波器輸出和算法的迭代公式。α穩(wěn)定分布噪聲背景下Mackey-Glass時(shí)間序列預(yù)測(cè)的仿真結(jié)果表明:KRLMP算法與KRLS算法和KRMC算法相比,抗脈沖噪聲能力強(qiáng),魯棒性好。
[Abstract]:The Kernel Recursive Least Square-KRLS (Kernel Recursive Least Square-KRLS) algorithm and Kernel Recursive Maximum Kernel Recursive Maximum Kernel Recursive Maximum KRMC (Kernel Recursive Maximum Kernel Kernel Recursive Maximum Kernel) algorithm seriously degrade the performance of nonlinear signal prediction in the background of strong impulse noise interference. In this paper, a kernel recursive minimum average P-norm Kernel Recursive Least Mean P-norm-KRLMPalgorithm is proposed. Firstly, the kernel method is used to map the input data to the regenerative kernel Hilbert space. Secondly, based on the minimum P-norm criterion and the regularization method, the optimal weight vector of the adaptive filter is derived, which reduces the influence of the non-Gao Si pulse and the small sample size. Then the recursive formula of matrix is derived by using matrix inverse theory. Finally, the iterative formula of the filter output and algorithm calculated efficiently in the input space is obtained by using the kernel technique. The simulation results of Mackey-Glass time series prediction under the background of 偽 stable distributed noise show that the Mackey-Glass algorithm is compared with the KRLS algorithm and the KRMC algorithm. Strong ability to resist impulse noise and good robustness.
【作者單位】： 杭州電子科技大學(xué)通信工程學(xué)院;中國(guó)電子科技集團(tuán)第36研究所通信系統(tǒng)信息控制技術(shù)國(guó)家級(jí)重點(diǎn)實(shí)驗(yàn)室;
【分類號(hào)】：O211.3;TN713
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本文編號(hào)：1900170