基于果蝇算法和灰色神经网络的电子装备故障预测方法研究

Research on Fault Prognostic Method of Electronic Equipment Based on FOA and GNN

摘要:

针对电子装备故障数据小样本、非线性的特点,在相空间重构处理原始时间序列数据的基础上,基于k折交叉验证和果蝇算法优化灰色神经网络模型参数,从而提出一种基于果蝇算法和灰色神经网络的故障预测方法,并以某型雷达高压电源监测数据仿真结果为例验证其模型性能；实验结果表明,相比已有方法,该方法在全局优化、收敛速度、预测精度方面都具有一定优势。

Abstract:

In view of the small sample and nonlinear characteristics of fault data in electronic equipment, the fault prognostic method of FOA and GNN is presented. Fruit fly optimization algorithm(FOA) and k-fold cross validation are applied to optimize the model parameters of Grey Neural Network(GNN) with the original time sequence data are reconstructed in the phase space . And the performance of the proposed model is validated by the simulation results of high voltage power supply. Compared to other methods, the proposed model has better global optimization, convergence speed and prediction accuracy.