The improvement of regular solution model based on genetic algorithm and neural network and its application in binary slag systems
文献类型:期刊论文
作者 | Wu Ling; Jiang Zhouhua; Gong Wei; Liang Lianke |
刊名 | ACTA METALLURGICA SINICA
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出版日期 | 2008 |
卷号 | 44期号:7页码:799-802 |
关键词 | slag regular solution model activity neural network genetic algorithm |
ISSN号 | 0412-1961 |
其他题名 | THE IMPROVEMENT OF REGULAR SOLUTION MODEL BASED ON GENETIC ALGORITHM AND NEURAL NETWORK AND ITS APPLICATION IN BINARY SLAG SYSTEMS |
英文摘要 | A real solution activity model was introduced by improving the components interaction energy in regular solution model with genetic algorithm (GA) and neural network (NN) and the activities of components in MnO-SiO2 and CaO-Al2O3 binary systems were estimated by this model. Due to the different properties between real solution and regular solution, it is proved that the interaction energy Omega(ij) is the function of temperature and composition, and Omega(ij) not equal Omega(ij) at the same temperature and composition. With the comparison of results received by calculation and previous studies, the improved model has a high nonlinear fitting capability, so it can be used to accurately predict the activity of solution component. |
语种 | 英语 |
CSCD记录号 | CSCD:3355058 |
源URL | [http://ir.imr.ac.cn/handle/321006/142470] ![]() |
专题 | 金属研究所_中国科学院金属研究所 |
作者单位 | 中国科学院金属研究所 |
推荐引用方式 GB/T 7714 | Wu Ling,Jiang Zhouhua,Gong Wei,et al. The improvement of regular solution model based on genetic algorithm and neural network and its application in binary slag systems[J]. ACTA METALLURGICA SINICA,2008,44(7):799-802. |
APA | Wu Ling,Jiang Zhouhua,Gong Wei,&Liang Lianke.(2008).The improvement of regular solution model based on genetic algorithm and neural network and its application in binary slag systems.ACTA METALLURGICA SINICA,44(7),799-802. |
MLA | Wu Ling,et al."The improvement of regular solution model based on genetic algorithm and neural network and its application in binary slag systems".ACTA METALLURGICA SINICA 44.7(2008):799-802. |
入库方式: OAI收割
来源:金属研究所
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