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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| Vydané v: | IEEE transactions on circuits and systems. II, Express briefs Ročník 67; číslo 7; s. 1344 - 1348 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| 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. |
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| 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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| 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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