Signal Integrity Analysis Based on SVR Improved Algorithm
Kaixing Cheng, Zhongqiang Luo, Xingzhong Xiong, Lin-Lin Cheng, Xiaohan Wei, Leilei Chen, Wei Zhang · 2021
Compared with the traditional support vector machine regression (SVR), the SVR hyperparameter fast optimization algorithm can improve the accuracy of the prediction results. However, the data shows that when the training sample is too large, it will increase the complexity of model learning, resulting in too long modeling time. Therefore, we refer to the most effective support vector set search method in the variable selection and sparse support vector machine (VS-SSVM) algorithm, and appropriately fit the “advantages” of these two algorithms to construct a fast optimization hyperparameter and sparse support vector machine (FOH-SSVM) algorithm. In this work, we use the algorithm to solve the problem of signal integrity. The experimental results show that the modeling time required by the FOH-SSVM algorithm is 1%, which greatly reduces the modeling time. At the same time, the prediction accuracy of the algorithm is increased by 8%, ensuring good prediction performance.