基于模糊神经网络的导弹动力系统状态预测

Prediction of the State of Missile Power System Based on Fuzzy Neural Network

在导弹的工作过程中,准确并快速地监控导弹动力系统的状态变化是提高导弹安全性、可靠性的重要环节。研究了基于补偿模糊逻辑与神经网络相结合的补偿模糊神经网络(CFNN)。利用CFNN学习速度快、学习过程稳定、全局动态优化运算等特点,建立导弹动力系统状态参数预测模型。仿真结果表明,该模型收敛性好、预测精度高,对导弹动力系统状态参数具有较好的预测能力。

Monitoring the states of missile power system exactly and timely is very important in process of missile working,which helps the missile works safely and reliably.The compensative fuzzy neural network(CFNN) based on compensative fuzzy logic and neural network and its study arithmetic are researched.Considering its features as fast speed,steady studying course,global dynamic optimization,CFNN is applied to establish missile power system states forecasting model.The result of simulation shows that the model has faster convergence and better forecasting precision,and it works well in the process of predicting missile power system states.