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Proceedings Paper

Application of simple dynamic recurrent neural networks in solid granule flowrate modeling
Author(s): Yun Du; Huiqin Sun; Qiang Tian; Haiping Ren; Suying Zhang
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Paper Abstract

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.

Paper Details

Date Published: 13 October 2008
PDF: 7 pages
Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71271O (13 October 2008); doi: 10.1117/12.806441
Show Author Affiliations
Yun Du, Hebei Univ. of Science and Technology (China)
Huiqin Sun, Hebei Univ. of Science and Technology (China)
Qiang Tian, Shijiazhuang Realty Administerial Office (China)
Haiping Ren, Shijiazhuang Realty Administerial Office (China)
Suying Zhang, Hebei Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7127:
Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence

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