摇摆条件下自然循环流量的混沌时间序列预测

Prediction about Chaotic Times Series of Natural Circulation Flow under Rolling Motion

采用相空间重构和支持向量机相结合的方法建立混沌时间序列预测模型,用该模型对冷却剂体积流量进行预测。应用粒子群算法对模型中参数取值进行同步优化后,预测值与实际测量值的平均相对误差为1.5%,相对精度为0.9879。结果表明,该模型能够用于摇摆条件下自然循环的冷却剂体积流量预测,且具有较高的精度和鲁棒性。

The paper have proposed a chaotic time series prediction model, which combined phase space reconstruction with support vector machines. The model has been used to predict the coolant volume flow, in which a synchronous parameter optimization method was brought up based on particle swarm optimization algorithm, since the numerical value selection of related parameter was a key factor for the prediction precision. The average relative error of prediction values and actual observation values was 1.5% and relative precision was 0.9879. The result indicated that the model could apply for the natural circulation coolant volume flow prediction under rolling motion condition with high accuracy and robustness.