摇摆流动不稳定性的遗传算法优化神经网络预测

摘要

摇摆工况下自然循环系统的流动不稳定性现象对船用核动力系统的安全性有着显著影响。结合神经网络和遗传算法，对复杂不稳定性行为的预测进行了优化。采用小数据量法计算了流量时间序列的最大Lyapunov指数，得到了时间序列的最大可预测时间。应用单隐层BP神经网络对流量变化进行了多步滚动预测，在步数较少时预测结果与实验结果符合较好。但由于BP神经网络存在陷入局部最优解的问题，为此采用遗传算法对神经网络的初始阈值和权值进行优化，从而改善了BP神经网络的非线性预测性能。本文结果为流动不稳定性的实时预测提供了一种易于实际应用且准确度较高的途径。%The flow instability of natural circulation system under rolling motion has a significant influence on the safety of marine nuclear power system .The predict complex instability of flow rate under rolling motion was optimized using genetic algorithm and neural networks .The largest Lyapunov exponent of the flow rate time series was calcu‐lated by small‐data method to acquire the maximum predictable time .The multi‐step prediction of the flow rate time series was achieved by back propagation (BP) neural network with single hidden layer .The forecast result agrees well with the experiment data for the prediction with small number of time steps . However , the BP neural network could be trapped in local optimal solution .To overcome this drawback ,genetic algorithm was applied to optimize the initial thresholds and weights of the BP neural network .Hence , the non‐linear prediction ability of BP neural network was largely improved .A practical and relatively accurate method for natural circulation flow insta‐bility prediction is provided .