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基于SMO-SVR的飞机舵面损伤故障趋势预测

, PP. 1300-1305

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Abstract:

飞机舵面出现损伤时,为了更准确的预测状态参量变化情况,提出了一种改进的序贯最小优化支持向量回归(SMO-SVR,SequentialMinimalOptimizationSupportVectorRegression)预测方法.采用改进C-C平均方法对多元时间序列进行相空间重构,以确定最优嵌入维数m和延迟时间τd.根据所求m和τd建立加权SVR预测模型,并调整了SMO算法的停机准则.利用区间自适应粒子群算法(IAPSO,IntervalAdaptiveParticleSwarmOptimization)优化SVR参数,以提高参数优化速度.为了验证改进算法的有效性,针对飞机方向舵损伤故障趋势进行了预测和分析,并与径向基函数神经网络(RBFNN,RadialBasisFunctionNeuralNetwork)方法进行了对比,仿真结果表明SMO-SVR预测模型具有很好的预测能力.

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