| 酸法地浸浸出液铀浓度神经网络预测模型的研究 |
| |
| 引用本文： | 雷林,雷泽勇. 酸法地浸浸出液铀浓度神经网络预测模型的研究[J]. 矿冶工程, 2007, 27(4): 17-20 |
| |
| 作者姓名： | 雷林 雷泽勇 |
| |
| 作者单位： | 南华大学,城市建设学院,湖南,衡阳,421001;南华大学,机械工程学院,湖南,衡阳,421001 |
| |
| 摘 要： | 影响酸法地浸浸出液铀浓度的因素很多, 采用传统方法难以建立精确的数学模型, 或模型预测精度不高。在揭示浸出液铀浓度混沌特性的基础上, 利用混沌特性处理输入样本及确定神经网络结构, 将神经网络与改进型遗传算法结合, 构建了基于改进型遗传算法的浸出液铀浓度神经网络预测模型。
|
| 关 键 词： | 酸法地浸 铀浸出 改进遗传算法 神经网络 浸出液浓度预测模型 |
| 文章编号： | 0253-6099（2007）04-0017-04 |
| 收稿时间： | 2007-03-05 |
| 修稿时间： | 2007-03-05 |
Neural Network Predicting Model for Uranium Concentration of Acid In-situ Leach Liquor |
| |
| LEI Lin,LEI Ze-yong. Neural Network Predicting Model for Uranium Concentration of Acid In-situ Leach Liquor[J]. Mining and Metallurgical Engineering, 2007, 27(4): 17-20 |
| |
| Authors: | LEI Lin LEI Ze-yong |
| |
| Affiliation: | 1. School of Urban Construction, Nanhua University, Hengyang 421001, Hunan, China; 2. School of Mechanical Engineering, Nanhua University, Hengyang 421001, Hunan, China |
| |
| Abstract: | Using traditional methods to build precise mathematical model is hard or make the model's prediction less precise because the uranium concentration of acid in-situ leach liquor is under the influence of many factors. The chaotic dynamic performance of uranium concentration of acid in-situ leach liquor is revealed. The chaotic performance is used to deal with input samples and determine the structure of neural network．The neural network is combined with improved genetic algorithm to construct a new predicting model for uranium concentration of acid in-situ leach liquor. |
| |
| Keywords: | acid in-situ leaching uranium leaching improved genetic algorithm neural network uranium concentration of acid in-situ leach liquor |
| 本文献已被 维普 万方数据 等数据库收录！ |
| 点击此处可从《矿冶工程》浏览原始摘要信息 |
|
点击此处可从《矿冶工程》下载全文 |
|
