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

Yun Du; Huiqin Sun; Qiang Tian; Haiping Ren; Suying Zhang

doi:10.1117/12.806441

13 October 2008 Application of simple dynamic recurrent neural networks in solid granule flowrate modeling

Yun Du, Huiqin Sun, Qiang Tian, Haiping Ren, Suying Zhang

Proceedings Volume 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence; 71271O (2008) https://doi.org/10.1117/12.806441
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

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.

Citation Download Citation

Yun Du, Huiqin Sun, Qiang Tian, Haiping Ren, and Suying Zhang "Application of simple dynamic recurrent neural networks in solid granule flowrate modeling", Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71271O (13 October 2008); https://doi.org/10.1117/12.806441

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