Improved PSO in Water Supply Systems Based on AHP-RS and RBF Neural Network

被引：0

作者：

Wang Aojie ^{[1
]}

Liu Chaolue ^{[1
]}

机构：

[1] Chongqing Univ, Jiangxi Yuzhou Sci & Technol Inst, Chongqing, Peoples R China

来源：

ARCHITECTURE AND BUILDING MATERIALS, PTS 1 AND 2 | 2011年 / 99-100卷

关键词：

PSO; Water Supply Systems; AHP-RS; RBF Neural Network;

D O I：

10.4028/www.scientific.net/AMM.99-100.199

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

A evaluation model based on the integration of analytic hierarchy process(AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk. The Particle Swarm Optimization (PSO) algorithm is implemented to optimize the node numbers of the hidden layers in the model. The study indicates that the AHP-RS and RBF neural network connecting with improved PSO method is an attractive alternative to the conventional regression analysis method in modeling water distribution systems.

引用

页码：199 / 202

页数：4

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