## Kernel Adaptive(커널 적응형)란 무엇입니까?

Kernel Adaptive 커널 적응형 - To alleviate this problem and realize as fully as possible the potential of SSIM, an anisotropic implementation is put forward in this letter in which a kernel adaptive to image local structures is integrated in SSIM computation.^{[1]}

이 문제를 완화하고 SSIM의 잠재력을 최대한 실현하기 위해 이미지 로컬 구조에 적응하는 커널이 SSIM 계산에 통합된 이방성 구현이 이 편지에서 제시됩니다.

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## kernel recursive least 커널 재귀 최소

ABSTRACT Aiming at the problems of strong nonlinearity and complicated mechanism for chemical processes, a class of soft sensor modeling methods based on kernel adaptive filtering (KAF) algorithms are proposed, including sliding-window kernel recursive least-squares (SW-KRLS), fixed-budget kernel recursive least-squares (FBKRLS) and quantization kernel least mean squares (Q-KLMS).^{[1]}To improve the robustness of the kernel recursive least squares algorithm (KRLS) and reduce its network size, two robust recursive kernel adaptive filters, namely recursive minimum kernel risk-sensitive mean p-power error algorithm (RMKRP) and its quantized RMKRP (QRMKRP), are proposed in the RKHS under the minimum kernel risk-sensitive mean p-power error (MKRP) criterion, respectively.

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요약 강한 비선형성과 화학 공정에 대한 복잡한 메커니즘의 문제를 목표로, 슬라이딩 윈도우 커널 재귀 최소 제곱(SW-KRLS), 고정형 최소 자승(SW-KRLS)을 포함한 커널 적응 필터링(KAF) 알고리즘을 기반으로 한 소프트 센서 모델링 방법의 클래스가 제안됩니다. 예산 커널 재귀 최소 제곱(FBKRLS) 및 양자화 커널 최소 평균 제곱(Q-KLMS).

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## kernel adaptive filter 커널 적응 필터

Therefore, for the accurate estimation of DOD, DOA and Doppler shift, an efficient, kernel adaptive filter (KAF) based estimation approach is proposed.^{[1]}Moreover, we emphasize recent advances of NLANC algorithms, such as spline ANC algorithms, kernel adaptive filters, and nonlinear distributed ANC algorithms.

^{[2]}The Cauchy loss (CL) is a high-order loss function which has been successfully used to overcome large outliers in kernel adaptive filters.

^{[3]}To further tackle complex nonlinear issues, novel multiple random Fourier features (MRFF) spaces are then constructed in finite-dimensional features spaces, which is proven effective for approximation of multi-kernel adaptive filter (MKAF), theoretically.

^{[4]}In this brief, a kernel adaptive filter based on the Student’s

^{[5]}The kernel adaptive filters (KAFs) can better solve the nonlinear problem by mapping the filtered reference signal to the high dimensional reproductive kernel Hilbert feature space (RKHFS).

^{[6]}The complex kernel adaptive filter (CKAF) has been widely applied to the complex-valued nonlinear problem in signal processing and machine learning.

^{[7]}The kernel least mean square (KLMS) algorithm is the simplest algorithm in kernel adaptive filters.

^{[8]}5 concentration called Weather Research and Forecasting model based quantized kernel adaptive filter (WRF-QKAF) is proposed in this paper.

^{[9]}A learning task is sequential if its data samples become available over time; kernel adaptive filters (KAFs) are sequential learning algorithms.

^{[10]}In this paper, we develop a kernel adaptive filter for quaternion domain data, based on information theoretic learning cost function which could be useful for quaternion based kernel applications of nonlinear filtering.

^{[11]}To improve the robustness of the kernel recursive least squares algorithm (KRLS) and reduce its network size, two robust recursive kernel adaptive filters, namely recursive minimum kernel risk-sensitive mean p-power error algorithm (RMKRP) and its quantized RMKRP (QRMKRP), are proposed in the RKHS under the minimum kernel risk-sensitive mean p-power error (MKRP) criterion, respectively.

^{[12]}Random Fourier mapping (RFM) in kernel adaptive filters (KAFs) provides an efficient method to curb the linear growth of the dictionary by projecting the original input data into a finite-dimensional space.

^{[13]}In this paper, we develop a kernel adaptive filter for quaternion data, using stochastic information gradient (SIG) cost function based on the information theoretic learning (ITL) approach.

^{[14]}Firstly, it is modified to operate as a kernel adaptive filter, i.

^{[15]}The proposed KMEC is derived in the context of the kernel adaptive filter and it provides good performance for identifying the nonlinear channels in different mixed noise environments in terms of the mean square error (MSE) at its steady-state and convergence performance.

^{[16]}For nonlinear channels, Kernel Adaptive Filters (KAFs) have been used since they are able to solve nonlinear problems implicitly projecting the input vector into a larger dimension space, where they can be linearly solved.

^{[17]}Kernel conjugate gradient (KCG) algorithms have been proposed to improve the convergence rate and filtering accuracy of kernel adaptive filters (KAFs).

^{[18]}In recent years, the kernel adaptive filter (KAF) has been widely adopted to solve the robust regression problem due to its low-complexity and high-approximation capability and robustness while the applications in battery RUL prediction are still few and far between.

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따라서 DOD, DOA 및 Doppler shift의 정확한 추정을 위해 효율적인 KAF(kernel 적응 필터) 기반 추정 접근 방식이 제안됩니다.

^{[1]}또한 스플라인 ANC 알고리즘, 커널 적응 필터 및 비선형 분산 ANC 알고리즘과 같은 NLANC 알고리즘의 최근 발전을 강조합니다.

^{[2]}코시 손실(CL)은 커널 적응 필터에서 큰 이상값을 극복하는 데 성공적으로 사용된 고차 손실 함수입니다.

^{[3]}복잡한 비선형 문제를 추가로 해결하기 위해 새로운 MRFF(다중 랜덤 푸리에 피쳐) 공간이 유한 차원 피쳐 공간에 구성되며, 이는 이론적으로 MKAF(다중 커널 적응 필터) 근사에 효과적인 것으로 입증되었습니다.

^{[4]}이 요약에서, 재생산 커널 Hilbert에서 학생의 <inline-formula> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> 분포를 기반으로 하는 커널 적응 필터 space(RKHS)는 다음과 같이 기존 커널 적응 필터링 알고리즘과 구별됩니다. 먼저 학생의 <inline-formula> <tex-math notation="LaTeX">${t}$ </tex-math> 커널 기능을 재생하는 </inline-formula>는 충동-가우시안 혼합 잡음 모델에 의해 묘사되는 가우스 잡음과 함께 갑작스러운 잡음에 대항하기 위해 제안됩니다. 두 번째로 제안된 Student의 <inline-formula> <tex-math notation="LaTeX" >${t}$ </tex-math></inline-formula> 기반 커널 필터.

^{[5]}커널 적응 필터(KAF)는 필터링된 참조 신호를 고차원 재생산 커널 힐베르트 특징 공간(RKHFS)에 매핑하여 비선형 문제를 더 잘 해결할 수 있습니다.

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## kernel adaptive filtering 커널 적응 필터링

In this paper, we propose a nonlinear recurrent kernel normalized LMS (NR-KNLMS) algorithm based on the algorithmic framework of multikernel adaptive filtering for nonlinear autoregressive systems.^{[1]}ABSTRACT Aiming at the problems of strong nonlinearity and complicated mechanism for chemical processes, a class of soft sensor modeling methods based on kernel adaptive filtering (KAF) algorithms are proposed, including sliding-window kernel recursive least-squares (SW-KRLS), fixed-budget kernel recursive least-squares (FBKRLS) and quantization kernel least mean squares (Q-KLMS).

^{[2]}In this paper, two new multi-output kernel adaptive filtering algorithms are developed that exploit the temporal and spatial correlations among the input-output multivariate time series.

^{[3]}The purpose of kernel adaptive filtering (KAF) is to map input samples into reproducing kernel Hilbert spaces and use the stochastic gradient approximation to address learning problems.

^{[4]}This paper presents an automatic kernel weighting technique for multikernel adaptive filtering.

^{[5]}Although the sparse kernel adaptive filtering algorithms have been proposed to address the problem of redundant dictionary in non-stationary environments, there is few attempt of analyzing their stochastic convergence behaviors.

^{[6]}An outlier detection and elimination method based on kernel adaptive filtering with variable step size for trajectory data of vehicle test was proposed.

^{[7]}Simulations are conducted to illustrate the performance benefits of RFF-EW-KRLP related to the typical kernel adaptive filtering algorithms based on the second statistic error criterion in the impulsive noise environment.

^{[8]}Next, borrowing sparsification methods from kernel adaptive filtering, the continuous action-space approximation in the online least-squares policy iteration algorithm can be efficiently automated as well.

^{[9]}Simulations demonstrate the EW-KRLP algorithm has better convergence performance than the existing kernel adaptive filtering approaches in identifying the non-stationary nonlinear system under the assumption of non-Gaussian impulsive noise modeled by the symmetric α-stable distribution.

^{[10]}We present an online method for multiscale data classification, using the multikernel adaptive filtering framework.

^{[11]}An interpolation method based on bidirectional unequal interval kernel adaptive filtering was proposed to solve the missing data problem of trajectory measurement for the vehicle test.

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본 논문에서는 비선형 자기회귀 시스템을 위한 다중 커널 적응 필터링 알고리즘 프레임워크를 기반으로 하는 NR-KNLMS(Nonlinear recurrent kernel normalized LMS) 알고리즘을 제안한다.

^{[1]}요약 강한 비선형성과 화학 공정에 대한 복잡한 메커니즘의 문제를 목표로, 슬라이딩 윈도우 커널 재귀 최소 제곱(SW-KRLS), 고정형 최소 자승(SW-KRLS)을 포함한 커널 적응 필터링(KAF) 알고리즘을 기반으로 한 소프트 센서 모델링 방법의 클래스가 제안됩니다. 예산 커널 재귀 최소 제곱(FBKRLS) 및 양자화 커널 최소 평균 제곱(Q-KLMS).

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