改进粒子群算法在乙烯收率软测量中的应用

针对粒子群算法(PSO)容易陷入局部收敛的问题,提出一种引入反动因子并结合引力定律的方法来改进算法,增强其寻优能力,该改进算法命名为:GPSO算法.该算法利用引力定律快速确定粒子的寻优方向,寻优过程中当粒子陷入局部最优时利用反动因子的引入使粒子跳出局部最优.仿真实验证明该改进算法在收敛速度和寻优能力上都取得了显著效果.最后,用改进的算法优化BP神经网络的参数,获得了乙烯裂解转化率模型,实验结果表明,基于改进算法的神经网络模型能够较好地预测乙烯裂解转化率.

Application of Improved Particle Swarm Algorithm in the Soft Measurement of Ethylene Yield

According to the problem that the particle swarm optimization(PSO) is easy to fall into local convergence, a new method named GPSO algorithm is proposed to improve the algorithm, which is based on the reaction factor and the law of gravity. The algorithm uses the gravity law to quickly determine the optimal direction of the particles. When the particles fall into local optimum, the particles are drapped out of local optimum. The simulation experiments show that the improved algorithm has achieved remarkable results in the convergence speed and the optimization ability. Finally, the model of ethylene cracking conversion was obtained by using the improved algorithm to optimize the parameters of BP neural network. The experimental results show that the neural network model based on the improved algorithm can better predict the conversion rate of ethylene.