基于改进组合神经网络的水资源预测研究.PDFVIP

Vo 1. 43 No. 6A 第 43 卷第6A 期 计算机科学 2016 年 6 月 Computer Science June 2016 基于改进组合神经网络的水资源预测研究 王坚 (中央财经大学信息学院 北京 100081) 摘 要 我国作为水资源大国，在日益加速的城镇化进程中正面临人口膨胀、环境污染、水质变差等一系列重大的挑 战，而科学合理地对水资源需求进行预测成为保护环境、保持可持续发展的关键任务。首先将神经网络应用于水资源 需求预测问题背景并比较其算法，同时引入模糊反馈法来改进:埔值法以确定纽合模型的加权系数，建立纽合神经网络 预测模型。该算法不仅可以根据历史数据自动推演今后水资源需求的变化趋势，还引入反馈和演化机制，用户可以调 整求解精度以控制算法的收敛速度。实验表明，在数据精度不高以及水文数据不全等不利应用背景中，提出的基于组 合模型的神经网络在水资源预测中具有较好的性能。 关键词 水资源需求预测，神经网络，组合模型，模糊反馈 中图法分类号 TN912.33 文献标识码 A Research 00 Predictioo of Water Resource ?ased 00 Improved Combioatioo Neural Network WANGJian CSchool of Infonnation ,Central University of Finance and Economics ,Beijing 100081 ,China) Abstract China is a big country of water resource and in the accelerating process of urbanization ,it is facing a series of major challenges such as urban population growth and water pollution ,etc ,so scientific and rational forecasting to water resource demand becomes a key task to protect environment and maintain sustainable development. This paper summa rized various neural network algorithms in the context of water resource demand forecasting ,and introduced fuzzy feed- back method to improve entropy method to determine the weighting factor of combination forecasting model ,to estab lish neural network forecasting mode l. The algorithm can not only automatically deduce future change trends of water resources based on historical data ,but also introduce feedback and evolution mechanism ,so that the users can adjust the solution accuracy to control convergence speed of algorithm. The exp