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

机译：具有在线多核RBF调整功能的新型自适应LSSVR，可评估模拟电路性能

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摘要

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 andε-SVR, which is suitable for promotion online.
机译：针对快速在线响应的模拟电路性能评估需求，提出了一种基于自适应最小二乘支持向量回归（LSSVR）的多核RBF评估方法。多内核RBF的优越性使在线内核功能（例如带宽调整）具有更大的灵活性。然后，内核参数的决策参数确定输入信号，以映射到特征空间，从而通过丢弃冗余特征推断出井厂模型。实验采用典型的电路Sallen-Key低通滤波器通过八个性能指标来证明所提出的评估策略。仿真结果表明，该方法的测试速度和评估性能，尤其是所提出的测试速度，优于传统的LSSVR和ε-SVR，适合在线推广。

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