【摘要】：為解決傳統(tǒng)凸組合自適應(yīng)濾波算法在聯(lián)合參數(shù)迭代計(jì)算量大、算法收斂速度慢、跟蹤性能差等問題,提出了一種基于一種新的S型函數(shù)快速凸組合最小均方(SCLMS)算法;該算法用一種新的S型函數(shù),代替Sigmoid函數(shù),在保證和CLMS算法相同穩(wěn)態(tài)誤差情況下,避免了指數(shù)運(yùn)算,減少了計(jì)算量;同時(shí)也提高了收斂速度和信號(hào)的跟蹤性能。通過獨(dú)立高斯白噪聲作為輸入信號(hào)算法仿真、相關(guān)噪聲作為輸入信號(hào)算法仿真;以及非平穩(wěn)環(huán)境下算法仿真;并對(duì)三種仿真結(jié)果進(jìn)行了分析,驗(yàn)證了該算法性能可靠有效。
[Abstract]:In order to solve the problems of the traditional convex combinatorial adaptive filtering algorithm, such as large computation complexity in joint parameter iteration, slow convergence speed and poor tracking performance, a new fast convex combined minimum mean square (SCLMS) algorithm based on S-type function is proposed. In this algorithm, a new S-type function is used to replace the Sigmoid function. Under the same steady state error as the CLMS algorithm, the exponential operation is avoided and the computational complexity is reduced, and the convergence rate and the tracking performance of the signal are also improved. By using independent Gao Si white noise as input signal algorithm simulation, correlation noise as input signal algorithm simulation, and algorithm simulation in non-stationary environment, three simulation results are analyzed, and the performance of the algorithm is verified to be reliable and effective.
【作者單位】： 廣西科技大學(xué)汽車與交通學(xué)院廣西汽車零部件與整車技術(shù)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金(51665006) 廣西高校自然科學(xué)基金(2013YB172)資助
【分類號(hào)】：TN713

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本文編號(hào)：2297692