Modeling for Production Rate Measurement of Hydrocyanic Acid Based on PSONN
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.
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