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Abstract and Applied Analysis
Volume 2013, Article ID 231735, 7 pages
Research Article

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

1College of Engineering, Bohai University, Jinzhou 121013, China
2Department of Engineering, Faculty of Engineering and Science, The University of Agder, 4898 Grimstad, Norway

Received 6 November 2013; Accepted 29 November 2013

Academic Editor: Ming Liu

Copyright © 2013 Aihua Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 -SVR, which is suitable for promotion online.