基于支持向量机的动态测量误差非线性组合预测方法

针对目前动态测量误差序列预测方法的局限性,提出了动态测量误差序列的支持向量机非线性组合预测方法,以进行误差修正,提高动态测量精度.该方法首先利用支持向量机和小波神经网络对动态测量误差序列分别进行预测,然后再运用支持向量机对单项预测结果进行非线性组合.理论分析和预测实例表明:该方法的预测精度明显高于传统的单一预测方法,具有很强的学习与泛化能力,在处理动态测量误差序列的预报问题和提高动态测量精度方面具有很好的应用价值.

A nonlinear combination forecasting method for dynamic measurement errors based on support vector machines

In view of the limitation of dynamic measurement errors forecasting methods, the paper puts forward a nonlinear combination forecasting method for dynamic measurement errors based on support vector machines to correct the measurement errors and improve the dynamic measurement accuracy. The method first makes use of support machines and wavelet nearal network to forecast the error series separately and then ulilizes the support vector machines to combine each forcasted result in a nonlinear way. The theoretical analysis and forecasted examples show that the method has powerful learning and universalized capability. Its forecasting accuracy is higher than single forecasting method. Thus it can be seen that the method is of great applied value.