Soft-sensing technology based on improved BP-neural-network

被引：0
|
作者：
Cao, Xianqing
Zhu, Jianguang
Tang, Renyuan
机构：
[1] Research Institute of Special Electric Machines, Shenyang University of Technology, Shenyang 110023, China
[2] Shenyang Institute of Chemical Technology, Shenyang 110142, China
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2005年 / 26卷 / SUPPL.期
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中图分类号：
学科分类号：
摘要：
It brought forward importing a factor of gradient to improve the mathematics on the base of the means of momentum and self-adapting modify learning rate, aimed at the training difficult to escape the flat area of error, and carry through a experiment on the process of synthetically hydrochloric acid with the improved BP-neural-network. Experimental results show this method not only can raise precision of the model, but also can enhance the ability of generalization.
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页码：185 / 186
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