基于偏最小二乘回归的神经网络与投影寻踪耦合模型在水资源承载力预测中的应用

Xiao-Yong Zhao
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引用次数: 1

摘要

偏最小二乘回归方法可以有效地处理自变量之间的多重共线性问题,但不能理想地解决因变量与自变量之间复杂的非线性问题。结合神经网络和投影寻踪的耦合模型方法是处理非线性问题的理想工具,具有很好的稳定性,但不能理想地解决自变量间的多重共线性问题。本文将这两种方法结合起来,建立了基于偏最小二乘回归的神经网络耦合模型和投影寻踪预测水资源承载力的方法。预测结果表明,该组合优于任意一种,该模型能给出满意的预测效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Coupling Model with Neural Network and Projection Pursuit Based on Partial Least-Squares Regression to Water Resources Carrying Capacity Forecasting
The method of partial least-squares regression can effectively deal with the problems of multicollinearity among independent variables", "but can not ideally solve the complicated problems of nonlinearity between dependent variables and independent variables. The method of coupling model with neural network and projection pursuit is an ideal tool to deal with the problem of nonlinearity, and it is very steady, but can not ideally solve the problems of multicollinearity among independent variables. The paper combines the two methods to establish the method of coupling model with neural network and projection pursuit based on partial least-squares regression for forecast water resources carrying capacity. the results of forecasting indicate that the combination is superior to either of them, the model was found to be able to give satisfactory effect.
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