Modeling for Production Rate Measurement of Hydrocyanic Acid Based on PSONN


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Aimed at the problem that it is difficult to measure production rate of hydrocyanic acid directly. So the soft measurement model of production rate of hydrocyanic acid can be established based on neural networks according to interrelated measurable engineering signals. Before being application to engineering, the soft measurement model is trained by PSO algorithm instead of the fast gradient descent method; Simulations prove that the soft measurement model trained by PSO possesses better measuring accuracy and stronger generalization ability. This kind of soft measurement model can be applied to practical production engineering of hydrocyanic acid.



Edited by:

Xiong Zhou and Zhenzhen Lei




Z. J. Tang and M. Song, "Modeling for Production Rate Measurement of Hydrocyanic Acid Based on PSONN", Applied Mechanics and Materials, Vol. 233, pp. 409-415, 2012

Online since:

November 2012




[1] Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 1995: 1942-(1948).

[2] Shi Y., Eberhart R. A modified particle swarm optimizer. Proceedings of the IEEE World Congress on Evolutionary Computation, 1998: 69-73.


[3] XIAO Jian-mei, LI Jun-jun, WANG Xi-huai. Convergence analysis of particle swarm optimization and its improved algorithm based on gradient, Control and Decision. 2009, 24(4): 560-564.


[4] LI Li, NIU Ben. Particle Swarm optimization. Bei Jing: Metallurgical Industry Press. (2009).

[5] DUAN Hai-bing, ZHANG Xiang-yin, XU Chun-fang. Bio-inspired Computing, Bei Jing: Scinence Press, (2011).

[6] CHEN Bao-di, ZENG Jian-chao. Modified attractive and repulsive particle swarm optimization, Control Theory & Applications, 2010, 27(4): 451-456.

[7] Li Jian Wang Cheng. A modified self-adaptive particle swarm optimization, Journal of Huazhong University of Science and Technology (Nature Science Edition). 2008, 36(3): 118-121.

[8] FENG Jun-qing, YU Zhi-hong. Application Of Data Classification Based on PSO And BP Neural Network,Techniques of Automation and Applications. 2007, 26(11): 13-15.