基于SVM的高耸圆形建筑物沉降数据分析及预报研究

随着高耸圆形建筑物大量修建,高耸圆形建筑物监测成为防治和减少灾害发生的一项极为重要的工作之一。针对小波降噪的平稳特性、小样本条件下支持向量机机器学习和预测的准确性,建立了高耸圆形建筑物的小波去噪、支持向量机机器学习和预测分析模型。实验表明,不确定性支持向量机的预测结果和学习效果的均方差比遗传算法更小,可即时发现高耸圆形建筑物不均匀下沉或倾斜现象。这种数据处理方法经试验表明可以应用到其他预测方面。

Research on settlement data analysis and prediction of the great tall circular buildings based on SVM

With more and more great tall circular buildings ,the related monitoring w hich can prevent and reduce the disasters is one of the extremely important work . For the stationary characteristic of the wavelet denoising and the accuracy of support vector machine learning and prediction at the small sample conditions ,a great tall circular buildings analysis model is proposed which uses the wavelet denoising and support vector machine learning and prediction .The experiment indicates that :the mean square error of support vector machine is smaller than the genetic algorithm at the predict outcomes and machine learning effect and can find the unbalance and tilt phenomenon of the great tall circular buildings immediately .The data processing of the test can be applied to other prediction .