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2013 A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance
Aihua Zhang, Chen Chen, Hamid Reza Karimi
Abstr. Appl. Anal. 2013(SI03): 1-7 (2013). DOI: 10.1155/2013/231735

Abstract

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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Aihua Zhang. Chen Chen. Hamid Reza Karimi. "A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance." Abstr. Appl. Anal. 2013 (SI03) 1 - 7, 2013. https://doi.org/10.1155/2013/231735

Information

Published: 2013
First available in Project Euclid: 26 February 2014

zbMATH: 1319.94118
Digital Object Identifier: 10.1155/2013/231735

Rights: Copyright © 2013 Hindawi

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Vol.2013 • No. SI03 • 2013
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