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New-ARMA-model-

于 2012-10-17 发布文件大小:9KB

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运用粒子群优化算法对ARMA(p,q)模型的阶数进行优化是可行的，且利用优化后的ARMA模型对航空发电机状态趋势进行分析的结果十分准确，与原始数据所表现出的航空发电机状态变化趋势基本一致。经计算，此时分析结果的平均相对误差较小，仅为1.38 ，与优化前构建的ARMA模型相比，平均相对误差降低0.23个百分点，优化效果明显。(Using particle swarm optimization algorithm to optimize the order of the ARMA (p, q) model is feasible, and the use of the results of the ARMA model optimized air generator status trends analysis is very accurate, and the raw data showed aviation the generator status consistent trend. Calculated analysis of the results, the average relative error is small, only 1.38 , compared with the the ARMA model built before optimization, average relative error is reduced by 0.23 percentage points, to optimize the effect is obvious.)

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