Application Research on Genetic Neural Network for Water Quality Comprehensive Evaluation

被引：0

作者：

Zhang, Lian ^{[1
]}

Li, Wen-juan ^{[1
]}

Zhang, Fang-fang ^{[1
]}

Lai, Wei ^{[1
]}

机构：

[1] Chongqing Univ Technol, Sch Elect Infomat & Automat, Chongqing, Peoples R China

来源：

PROCEEDINGS OF 2010 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (ICCSIT 2010), VOL 8 | 2010年

关键词：

BP Neural Network; Genetic Algorithm; Golden Section Theory; Water Quality Evaluation;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In order to overcome the shortcomings of BP neural network, the golden section theory was used to get the reasonable number of Back Propagation (BP) neural network's hidden nodes. By using Genetic Algorithm (GA) to optimize the initial weights and threshold value of BP neural network, the network converged quickly and the recognition precision was increased. The GA-BP neural network model was utilized to evaluate the water quality degree of 5 rivers in Xindu area of Chengdu, the evaluation results were compared with the results of single factor method and comprehensive pollution evaluation method. It indicated that the GA-BP neural network model has practicability, objectivity and generality in the application of water quality comprehensive evaluation.

引用

页码：636 / 640

页数：5

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