基于改进变维分形理论的拱坝温度监测数据预测模型

Forecasting Model of Monitored Data of Arch Dam's Temperature Based on Improved Variable Dimension Fractal Theory

目前用于温度监测数据预测的模型,对数据的长度有较强的依赖性,监测数据量较少时,预测效果不理想,在分形理论的基础上,尝试建立改进的变维分形预测模型,并以白莲崖拱坝温度监测数据为例进行分析、预测。结果证明,这种模型发挥分形理论有相似性的特点,克服了其他模型对数据长度的依赖性和噪声干扰对预测效果的影响,能较好应用于小数据量监测数据的预测,精度较高,有着良好的抗噪性。

At present, the forecasting model of temperature is mainly dependent on the number of monitored data, the prediction effect is not good as the monitored data are less. The paper attempts to set up and improve variable dimension fractal of forecasting model based on fractal theory, and analyzes the monitored data of Bailianya Arch Dam＇ s temperature. The forecasted result shows that the improved model takes advantage of the feature of fractal theory self-similarity, and decreases the dependence on the data quantity and the noise influence on prediction effect. The model is of high precision, and has vast noise immunity.