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A chaos genetic algorithm for optimizing an artificial neural network of predicting silicon content in hot metal 被引量:3
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作者 Deling Zheng, Ruixin Liang, Ying Zhou, and Ying WangInformation Engineering School, University of Science and Technology Beijing, Beijing 100083, China 《Journal of University of Science and Technology Beijing》 CSCD 2003年第2期68-71,共4页
A genetic algorithm based on the nested intervals chaos search (NICGA) hasbeen given. Because the nested intervals chaos search is introduced into the NICGA to initialize thepopulation and to lead the evolution of the... A genetic algorithm based on the nested intervals chaos search (NICGA) hasbeen given. Because the nested intervals chaos search is introduced into the NICGA to initialize thepopulation and to lead the evolution of the population, the NICGA has the advantages of decreasingthe population size, enhancing the local search ability, and improving the computational efficiencyand optimization precision. In a multi4ayer feed forward neural network model for predicting thesilicon content in hot metal, the NICGA was used to optimize the connection weights and thresholdvalues of the neural network to improve the prediction precision. The application results show thatthe precision of predicting the silicon content has been increased. 展开更多
关键词 blast furnace OPTIMIZATION chaos genetic algorithm neural network silicon content prediction
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