一种混合粒子群优化的沉降ARMAX模型

A Settlement ARMAX Model Based on Hybird Particle Swarm Optimization

为了提高氧化铝生产质量和降低能耗,分析了氧化铝沉降工艺中影响沉降过程的各种因素,采用系统辨识的方法建立沉降系统的带外部输入的自回归滑移ARMAX模型。为此提出了基于二阶混沌的混合粒子群算法,解决了粒子群算法容易早熟以及全局寻优效率偏低等问题,进而建立了基于二阶混沌的混合粒子群优化算法的沉降槽密度ARMAX模型。仿真实验表明,该混合粒子群算法的ARMAX模型可以对沉降过程中的槽内密度进行准确识别,指导氧化铝的沉降生产操作。

With a view to improving the quality and reducing the energy consumption,the factors influencing alumina settlement process were analyzed,and basing upon the system identification method,the auto-regressive moving average exogenous（ARMAX） model for the settlement system was established;and then,the quadratic chaotic-based hybird particle swarm optimization algorithm was presented to solve the prematurity of particle swarm algorithm and the low efficiency of global optimization;and finally,grounded on the hybrid particle swarm optimization and the quadratic chaotic,the settlement density＇s ARMAX model was built.The simulation results show that the ARMAX model based on the presented hybrid particle swarm algorithm can accurately identify the settlement process density,and the settlement of alumina production operations can be guided.