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针对变形分析及变形预测中非平稳非线性且含噪声的数据处理问题,该文采用经验模态分解技术提取反映变形趋势的低频逼近信号,利用傅里叶函数对低频逼近信号进行曲线拟合,并据此构建变形值预测模型,对出现的模型系数采用遗传算法求解。实例仿真表明所得预测模型具有较高的精确度,能满足工程需要。EMD-Fourier模型实质上是初等数学模型,具有结构简单、运算结果稳定、操作方便等优点。
Aiming at the problem of non-stationary and non-linear noise-containing data processing in deformation analysis and deformation prediction, this paper uses EMD to extract the low-frequency approximation signals that reflect the deformation trend, and uses the Fourier function to curve fitting the low-frequency approximation signals. Based on this, a deformation forecasting model is constructed, and the model coefficients that appear are solved by genetic algorithm. The simulation results show that the obtained prediction model has higher accuracy and can meet the needs of engineering. EMD-Fourier model is essentially an elementary mathematical model, with the advantages of simple structure, stable operation result and convenient operation.