|
|||||
|
|
| 改进遗传神经网络在水质评价中的应用 | |
| 引用本文： | 曾文华,崔侠,刘峰. 改进遗传神经网络在水质评价中的应用[J]. 重庆工商大学学报(自然科学版), 2012, 29(2): 47-51 |
| 作者姓名： | 曾文华 崔侠 刘峰 |
| 作者单位： | 1. 广州奥格智能科技有限公司,广州,510663 2. 广州市环境保护科学研究院,广州,510620 3. 广州地理研究所广州510070 |
| 基金项目： | 科技型中小企业技术创新基金资助 |
| 摘 要： | 根据地表水环境质量评价标准,选取5项监测指标作为评价因子,在各指标对应级别取值区间内按均匀分布方式随机内插生成训练样本,采用改进自适应交叉和变异算子的遗传算法对BP网络连接权和阈值进行优化,以长江口某河段水质评价为例,阐明了该方法在水质评价中的作用,并与综合指标法进行比较;结果表明:该方法具有较好的客观性和实用性,能有效地对地表水环境质量进行评价,丰富了水质评价的方法体系,为地表水环境管理与决策的提供依据。 |
| 关 键 词： | 遗传算法 神经网络 水质评价 |
Application of the Neural Networks Using Improved Genetic Algorithm to the Surface Water Quality Evaluation |
|
| ZENG Wen-hu,CUI Xi,LIU Feng. Application of the Neural Networks Using Improved Genetic Algorithm to the Surface Water Quality Evaluation[J]. Journal of Chongqing Technology and Business University:Natural Science Edition, 2012, 29(2): 47-51 | |
| Authors: | ZENG Wen-hu CUI Xi LIU Feng |
| Affiliation: | 1.Augur Intelligence Technology(Guangzhou) Co.,Ltd.,Guangzhou 510663,China; 2.Guangzhou Research Institute of Environmental Protection,Guangzhou 510620,China; 3.Guangzhou Institute of Geography,Guangzhou 510070,China) |
| Abstract: | Five indicators were selected as the evalulation factors according to Environmental Quality Standards for Surface Water,and the learning and training samples were created by uniform distributions random interpolating within the value of indicators.An improved genetic algorithm was used to optimize the initial weights and threshold and compared with comprehensive index method ,the precision of the model was verified... |
| Keywords: | genetic algorithm neural network surface water quality evaluation |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |
| 点击此处可从《重庆工商大学学报(自然科学版)》浏览原始摘要信息 | |
| 点击此处可从《重庆工商大学学报(自然科学版)》下载全文 | |