基于离散过程神经元的乙烯生产装置软测量

针对传统M-P神经网络模型的时间依赖性问题,提出将离散过程神经元应用到乙烯裂解炉软测量中,并将Fletcher-Reeves修正的改进变梯度学习算法应用到离散过程神经元网络,达到提高过程神经元网络的训练速度的目的。最后用乙烯装置的生产数据进行仿真研究,仿真结果表明该改进算法具有明显的快速收敛性,实现了乙烯产率的预测。

Soft Sensing of Ethylene Plant Based on the Discrete Process Neural

For the time-dependent problem of M-P neural network model,applied the discrete process neural to the ethylene plant soft sensing,and the application of the Alternating Gradient training algorithm amended by Fletcher-Reeves algorithm on the discrete process of neural network.Finally,use the ethylene production plant production data to emulate.Simulation results show that the improved algo-rithm has obvious rapid convergence.Achieve a prediction of ethylene production.