基于混合优化策略的自回归-滑动平均模型建模

自回归一滑动平均(ARMA)模型参数估计一直是ARMA模型建模问题的难点和重点,目前的模型参数估计方法都采用传统最小二乘法及其推广算法,预测精度低.采用基于混合优化策略的遗传模拟退火算法进行ARMA模型参数估计,克服了传统算法的缺点,并在此基础上利用遗传模拟退火算法可以确定ARMA阶次的特点,提出基于混合优化策略的ARMA模型建模方法.利用这种建模方法和传统建模方法对组合炮控系统精度进行建模比较,证明基于混合优化策略的ARMA模型建模方法收敛快,精度高.

MODELING OF AUTO-REGRESSIVE MOVING-AVERAGE(ARMA) BASED ON HYBRID OPTIMIZATION STRATEGY

Estimating parameters of auto-regressive mov- ing-average(ARMA) model is the focus of ARMA. The disad- vantages of least-squares algorithm and its generalization algo- rithm which are used in estimating parameters of ARMA are aimed at. Simulated annealing genetic algorithm based on hy- brid optimization strategy is used in estimating parameters of ARMA and it can overcome the disadvantages of the traditional methods. Based on the new algorithm, a new method of model- ing ARMA is presented by determinating the autoregressive orders p and moving-average orders q in ARMA model. Finally, the precision data ARMA model of a mechanical system is built by the new technology and by the traditional modeling method. The new technology proves effective and high-precision by comparing the two models.