一种粒子群模糊支持向量机的航天器参量预测方法

针对航天器精确预测与健康管理的需求，将粒子群算法、模糊数学与支持向量机的优势相结合，提出了一种粒子群模糊支持向量机预测方法。针对某卫星南帆板输出电流参量的预测实例，设计了总平均绝对误差、总平均绝对百分比误差、总均方根误差三个预测结果评价指标，对不同步长情况下的预测结果进行了比较，证明了粒子群优化模糊支持向量机预测方法的有效性。通过对比粒子群优化模糊支持向量机模型、灰色粒子群神经网络优化模型、粒子群神经网络模型、灰色模型预测的总平均绝对百分比误差，结果证明粒子群优化模糊支持向量机的预测精度和效率较高，在航天器参量预测领域具有较好的应用前景。

Particle Swarm Optimization Fuzzy Support Vector Machine Based Prediction of Spacecraft Parameters

According to requirements for accurate prediction and health management of spacecraft, a method of combinational prognosis of parameter values called particle swarm optimization-fuzzy support vector machines is proposed. The method makes respective advantages of particle swarm optimization algorithms, fuzzy mathematics and support vector machines complementary to each other. Incorporating with an example of prognosis of values of output current of the southern solar array of a certain satellite, three evaluation indexes of prognosis, including mean absolute error, mean absolute percentage error and root mean square error, are designed to evaluate prediction results of particle swarm optimization-fuzzy support vector machines at different step-lengths. The result shows that prognosis method of particle swarm optimization-fuzzy support vector machines is effective. The mean absolute percentage errors of particle swarm optimization-fuzzy support vector machines, grey particle swarm optimization neural network model, particle swarm optimization neural network model and grey model are calculated. The result shows that the model of particle swarm optimization-fuzzy support vector machines is most accurate and more efficient in prognosis. It has broad application prospects in the field of prognosis of spacecraft parameters.