控制点优化磨光算法在长江径流量预测中的应用

Controlpoints Optimization Smoothing Algorithm and Its Application in Predicting Runoff of Yangtze River

盈亏修正磨光法所得到的逼近效果仍然很差,通过控制点的参数优化和目标函数的最小,提出一种控制点优化磨光算法,利用这个算法得到参数后代入模型,使预测的精度得到提高.通过实例,该算法简单易行,并通过相对误差进行了分析,控制点优化磨光算法所得到的预测值好于神经网络模型、PPAR和小波网络模型的预测值,这为研究磨光法提供了较好的分析方法.

Approximation effects got by the profit and loss modified smoothing method are still very poor, through parameter optimization by controlpoints and the minimum of the objective function, a controlpoints optimization smoothing algorithm is outlined, the parameter got bye the algorithm substitute model, prediction accuracy is improved. The example express the algorithm is simple and easy, through An analysis of the relative errors, predicted value got bye controlpoints optimization smoothing algorithm is better than that of Neural network model, PPAR and Wavelet network model. It provides a better analysis method for studying smoothing method.