A new adaptive LSSVR with online multikernel RBF tuning to evaluate analog circuit performance
type: Item
instance of: scholarly article
title: A new adaptive LSSVR with online multikernel RBF tuning to evaluate analog circuit performance
zbMATH Open document ID: 1319.94118
DOI: 10.1155/2013/231735
author: Chen Chen
author: Hamid Reza Karimi
author: Aihua Zhang
published in: Abstract and Applied Analysis
publication date: 2014-06-23T00:00:00Z
review text: Summary: 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 \(\varepsilon\)-SVR, which is suitable for promotion online.
Mathematics Subject Classification ID: 94C05
zbMATH DE Number: 6306546
Wikidata QID: Q58915605
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/231735
OpenAlex ID: W2156258820
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description: scientific article; zbMATH DE number 6306546