基于振幅压缩的随机振荡序列预测模型

Prediction model of stochastic oscillation sequence based on amplitude compression

以提高灰色系统预测模型对随机振荡序列的预测精度为目的,提出了一种通过平滑性算子压缩随机振荡序列振幅,提高序列光滑度的算法,并在此基础上推导及建立随机振荡序列的灰色预测模型;将该模型应用于多组随机振荡序列的模拟,并与其他模型的模拟精度进行了比较,结果表明,新模型能显著提高随机振荡序列的模拟精度.

In order to improve the predictive accuracy of grey system prediction model with a stochastic oscillation sequence, this paper proposes an algorithm which can compress the amplitude and improve the smoothness of a stochastic oscillation sequence through a smoothness operator, and then deduces and establishes the prediction model of stochastic oscillation sequence. Through some examples＇ simulation, this paper compares simulation errors of this model with others; the results show that this novel method can evidently improve the simulative accuracy of stochastic oscillation sequence.