基于相空间重构的自适应残差修正支持向量回归预测算法

针对模拟电路故障预测存在的非线性时间序列预测问题和传统支持向量回归（SVR）多步预测时出现的误差累积问题，提出了一种基于相空间重构的自适应残差修正SVR预测算法。首先，分析了SVR多步预测方法对时间序列趋势预测的意义和多步预测导致的误差积累问题；其次，将相空间重构技术引入SVR预测中，对表征模拟电路状态的时间序列进行相空间重构，并进而进行SVR预测；然后，在对多步预测过程中产生的误差累积序列进行二次SVR预测的基础上，实现对初始预测误差的自适应修正；最后，对所提算法进行了预测仿真验证。仿真验证和模拟电路的健康度预测实验结果表明，所提算法能有效降低多步预测导致的误差积累，显著提高回归估计精度，更好地预测模拟电路状态的变化趋势。

Adaptive residual error correction support vector regression prediction algorithm based on phase space reconstruction

Focusing on the problem of nonlinear time series prediction in the field of analog circuit fault prediction and the problem of error accumulation in traditional Support Vector Regression (SVR) multi-step prediction, a new adaptive SVR prediction algorithm based on phase space reconstruction was proposed. Firstly, the significance of SVR multi-step prediction method for time series trend prediction and the error accumulation problem caused by multi-step prediction were analyzed. Secondly, phase space reconstruction technique was introduced into SVR prediction, the phase space of the time series of the analog circuit state was reconstructed, and then the SVR prediction was carried out. Thirdly, on the basis of the two SVR prediction of the error accumulated sequence generated in the multi-step prediction process, the adaptive correction of the initial prediction error was realized. Finally, the proposed algorithm was simulated and verified. The simulation verification results and experimental results of the health degree prediction of the analog circuit show that the proposed algorithm can effectively reduce the error accumulation caused by multi-step prediction, and significantly improve the accuracy of regression estimation, and better predict the change trend of analog circuit state.