用神经网络─遗传算法优化MgO-B_2O_3─SiO

金属学报, 1995, 31(18): 284-288.

用神经网络─遗传算法优化MgO-B_2O_3─SiO_2渣系组成

张培新 , 张奇志 , 吴黎明 , 隋智通

东北大学

基金项目: 国家自然科学基金

关键词：神经网络 , genetic algorithm , optimization of composition , boron , MgO-B_2O_3 SiO_2 slag

OPTIMIZATION OF MgO-B_2O_3-SiO_2 SLAGGING USING ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM

ZHA NG Peixin , ZHA NG Qizhi , W U Liming , SUI Zhitong(Northeasiern Unirersity.Shenyang 110006)

Keywords： : artificial neural network , 遗传算法 , 组成 , 优化 , ＭｇＯ－Ｂ_２Ｏ_３－ＳｉＯ_２渣系

应用人工神经网络对ＭｇＯ－Ｂ_２Ｏ_３－ＳｉＯ_２渣系组成与硼提取率关系进行拟合和预测，首次采用遗传算法对组成优化，并得到最佳硼提取率所对应的组成。

The relation among the MgO-B2O3-SiO2 slag compositions and the efficiencies of extraction of B has been fitted and predicated by artificial neural network. The optimum composition corresponding to the highest efficiency of extraction of B was obtained using genetic algorithm. It is believed that the artificial neural network and genetic algorithm may provide a new and effective way for fitting and optimizing the process of extraction of B.

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