Predictive Model Based on Artificial Neural Net for Purity of the Artificial Synthetic Hydrotalcite

摘要：

A prediction model for purity of the artificial synthetic hydrotalcite under varied process parameters based on artificial neural net was developed. And the non-linear relationship between the hydrotalcite purity and the raw material amount of NaOH、Mg2、Al3 was established based on BP learning algorithm analysis and convergence improvement. The hydrotalcite purity can be predicted by means of the trained neural net from the testing data. The learning algorithm for neural net is BP (backpropagation) algorithm with 3-2-1 structure The results show that, for multi-factor synthesis prediction, the prediction model based on BP learning algorithm for hydrotalcite purity of the prio-synthesis hydrotalcite is feasible and effective. Thus, by virtue of the prediction model, the future hydrotalcite purity can be evaluated under random complicated raw material amounts.

关键词： artificial synthesis hydrotalcite,neural net purity raw material adding amount

作者: Ren Qingli Luo Qiang He Bin

作者单位: Xian Jiaotong University, Xian, 710049, China P.R. Xidian University, Xian, 710071, China P.R. The Second Artillery Engineering College, Xian, 710025, China P.R.

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 1504-1508

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）