基于混沌神经网络的防空火箭炮交流伺服系统状态预测研究  

State Forecasting Research of Air-defense Rocket Launcher AC Servo System Based on Chaotic Neural Network

作　　者：胡健[1] 马大为[1] 姚建勇[1] 刘龙[1] 

机构地区：[1]南京理工大学机械工程学院,江苏南京210094

出　　处：《兵工学报》2015年第2期220-226,共7页Acta Armamentarii

基　　金：国家自然科学基金项目(51305203);"十二五"国防基础科研项目(B2620110005);江苏省博士后基金项目(1302002A)

摘　　要：为了更加准确地对系统非线性非平稳状态趋势进行预测,运用基于神经网络的混沌预测方法对防空火箭炮交流伺服系统的速度量进行了预测,为基于速度预测值的系统非线性非平稳状态趋势预测奠定了基础。利用C-C法选择了合适的嵌入维和时间延迟,对防空火箭炮交流伺服系统不规则运动的实验数据进行了相空间重构并进行了分析。在原Elman网络中增加了输出层关联单元,并把自反馈增益系数当作连接权值投入到网络的训练中,以增强Elman网络非线性逼近能力,在此基础上建立了基于改进型Elman网络的混沌预测模型。采用基于最大Lyapunov指数预测法和混沌神经网络预测法对系统状态进行了预测,两种方法的预测结果表明,后一种方法对防空火箭炮交流伺服系统速度值预测精度更高,从而使得基于此的系统非线性非平稳状态趋势预测更有效。To predict the system nonlinear and non-stationary conditions more accurately, a method of chaos prediction based on chaotic neural networks is introduced to predict the velocity of the air-defense rocket launcher AC servo system, which paves the way for the trend prediction of system nonlinear and non-stationary conditions. C-C method is used to select the proper embedding dimension and time delay. The phase space of system is reconstituted using experimental data of system＇ s irregular movement and is analyzed. The context neurons of output layer are added to the original Elman network, and the self-feedback gain coefficients are trained as connective weight, which could strengthen the nonlinear approximation ability of Elman network. Then a model of chaotic neural network based on the improved Elman network is set up. The predictions based on the maximun Lyapunov exponent and chaotic neural network are performed, respectively. The predicted results show that the prediction based on the chaotic neural network has a higher accuracy, which makes the trend forecasting of the system more effectively.

关 键 词：兵器科学与技术 火箭炮 交流伺服系统 状态预测 相空间重构 混沌神经网络 

分 类 号：TJ713[兵器科学与技术—武器系统与运用工程]