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KernelAdaptiveFiltering-master

于 2019-01-21 发布
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说明:  在信號處理中,核自適應濾波器是一種非線性自適應濾波器。[1]一種自適應濾波器是適應其一個濾波器傳遞函數通過最小化錯誤或隨時間的信號特性的變化的損失函數表徵濾波器從理想行為多遠偏離。自適應過程基於從一系列信號樣本中學習,因此是在線算法。非線性自適應濾波器是傳遞函數是非線性的。 核自適應濾波器使用核方法實現非線性傳遞函數。[1]在這些方法中,信號被映射到高維線性特徵空間,非線性函數被近似為核的總和,其域是特徵空間。如果這是在再生核Hilbert空間中完成的,則核方法可以是非線性函數的通用逼近器。內核方法具有凸損函數的優點,沒有局部最小值,並且實現起來只有中等複雜度。(State-space recursive least-squares (SSRLS) is optimal a linear estimator for deterministic signals. The performance of SSRLS however, depends on model uncertainty, time-varying nature of the observed signal or nonstationary behavior of the observation noise)

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