A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance

Focusing on the analog circuit performance evaluation demand of fast time responding online, a novel evaluation strategy based on adaptive Least Squares Support Vector Regression (LSSVR) which employs multikernel RBF is proposed in this paper. The superiority of the multi-kernel RBF has more flexibi...

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Main Authors: Aihua Zhang, Chen Chen, Hamid Reza Karimi
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2013/231735
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author Aihua Zhang
Chen Chen
Hamid Reza Karimi
author_facet Aihua Zhang
Chen Chen
Hamid Reza Karimi
author_sort Aihua Zhang
collection DOAJ
description Focusing on the analog circuit performance evaluation demand of fast time responding online, a novel evaluation strategy based on adaptive Least Squares Support Vector Regression (LSSVR) which employs multikernel RBF is proposed in this paper. The superiority of the multi-kernel RBF has more flexibility to the kernel function online such as the bandwidths tuning. And then the decision parameters of the kernel parameters determine the input signal to map to the feature space deduced that a well plant model by discarding redundant features. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the testing speed together with the evaluation performance, especially the testing speed of the proposed, is superior to that of the traditional LSSVR and ε-SVR, which is suitable for promotion online.
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publisher Wiley
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series Abstract and Applied Analysis
spelling doaj-art-3d7ee143c2034664bfa56025955da0ef2025-08-20T02:07:48ZengWileyAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/231735231735A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit PerformanceAihua Zhang0Chen Chen1Hamid Reza Karimi2College of Engineering, Bohai University, Jinzhou 121013, ChinaCollege of Engineering, Bohai University, Jinzhou 121013, ChinaDepartment of Engineering, Faculty of Engineering and Science, The University of Agder, 4898 Grimstad, NorwayFocusing on the analog circuit performance evaluation demand of fast time responding online, a novel evaluation strategy based on adaptive Least Squares Support Vector Regression (LSSVR) which employs multikernel RBF is proposed in this paper. The superiority of the multi-kernel RBF has more flexibility to the kernel function online such as the bandwidths tuning. And then the decision parameters of the kernel parameters determine the input signal to map to the feature space deduced that a well plant model by discarding redundant features. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the testing speed together with the evaluation performance, especially the testing speed of the proposed, is superior to that of the traditional LSSVR and ε-SVR, which is suitable for promotion online.http://dx.doi.org/10.1155/2013/231735
spellingShingle Aihua Zhang
Chen Chen
Hamid Reza Karimi
A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance
Abstract and Applied Analysis
title A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance
title_full A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance
title_fullStr A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance
title_full_unstemmed A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance
title_short A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance
title_sort new adaptive lssvr with online multikernel rbf tuning to evaluate analog circuit performance
url http://dx.doi.org/10.1155/2013/231735
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