The Nearest-Instance-Centroid-Estimation Kernel Recursive Least Squares Algorithms

The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel adaptive filters. However, the minimum mean square error (MMSE) criterion used in NICE-KLMS leads to performance degradation in some nonlinea...

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Veröffentlicht in:IEEE transactions on circuits and systems. II, Express briefs Jg. 67; H. 7; S. 1344 - 1348
Hauptverfasser: Zhang, Haonan, Wang, Lin, Zhang, Tao, Wang, Shiyuan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive filters
Algorithms
Centroids
Circuits and systems
Clustering algorithms
Computer simulation
Dictionaries
Kernel
kernel recursive least squares algorithm
Kernels
Least squares
Mean square error methods
minimum mean square error
Nearest-instance-centroid-estimation
nonlinear problems
Performance degradation
Prediction algorithms
quantization
Signal processing algorithms
Vector quantization
ISSN:1549-7747, 1558-3791
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Abstract The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel adaptive filters. However, the minimum mean square error (MMSE) criterion used in NICE-KLMS leads to performance degradation in some nonlinear problems. In this brief, the NICE is developed under the least-squares errors in the kernel space, generating a novel NICE kernel recursive least squares (NICE-KRLS) algorithm for performance improvement of NICE-KLMS. The weight update form for the solution to the least-squares errors existing in NICE-KRLS is therefore obtained recursively. To obtain a sparsification network, the vector quantization is combined into NICE-KRLS for online applications. Simulations on chaotic time-series prediction validate the superiorities of the proposed NICE-KRLS and its sparsification version.
AbstractList The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel adaptive filters. However, the minimum mean square error (MMSE) criterion used in NICE-KLMS leads to performance degradation in some nonlinear problems. In this brief, the NICE is developed under the least-squares errors in the kernel space, generating a novel NICE kernel recursive least squares (NICE-KRLS) algorithm for performance improvement of NICE-KLMS. The weight update form for the solution to the least-squares errors existing in NICE-KRLS is therefore obtained recursively. To obtain a sparsification network, the vector quantization is combined into NICE-KRLS for online applications. Simulations on chaotic time-series prediction validate the superiorities of the proposed NICE-KRLS and its sparsification version.
Author Zhang, Haonan
Wang, Shiyuan
Wang, Lin
Zhang, Tao
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Snippet The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel...
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SubjectTerms Adaptive filters
Algorithms
Centroids
Circuits and systems
Clustering algorithms
Computer simulation
Dictionaries
Kernel
kernel recursive least squares algorithm
Kernels
Least squares
Mean square error methods
minimum mean square error
Nearest-instance-centroid-estimation
nonlinear problems
Performance degradation
Prediction algorithms
quantization
Signal processing algorithms
Vector quantization
Title The Nearest-Instance-Centroid-Estimation Kernel Recursive Least Squares Algorithms
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