Application of simple dynamic recurrent neural networks in solid granule flowrate modeling

被引：0

作者：

Du Yun ^{[1
]}

Sun Huiqin ^{[1
]}

Tian Qiang ^{[2
]}

Ren Haiping ^{[2
]}

Zhang Suying ^{[1
]}

机构：

[1] Hebei Univ Sci & Technol, Coll Elect Engn & Informat Sci, Shijiazhuang 050054, Peoples R China

[2] Shijiazhuang Realty Adm Off, Shijiazhuang 050081, Peoples R China

来源：

SEVENTH INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND CONTROL TECHNOLOGY: SENSORS AND INSTRUMENTS, COMPUTER SIMULATION, AND ARTIFICIAL INTELLIGENCE | 2008年 / 7127卷

关键词：

SRNN; solid granule flowrate; RPE algorithm;

D O I：

10.1117/12.806441

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To build the solid granule flowrate model by the simple dynamic recurrent neural network (SRNN) is presented in this paper. Because of the dynamic recurrent neural network has the characteristic of intricate network structure and slow training algorithm rate, the simple recurrent neural network without the weight values on recursion layer is studied. The recurrent prediction error (RPE) learning algorithm for SRNN by adjustment the weight value and the threshold value is reduced. The modeling result of solid granule flowrate indicates that it has fast convergence rate and the high precision the model. It can be used on real time.

引用

页数：7

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