应用递推神经网络的传感器动态建模研究

根据动态校准实验结果建立传感器的动态数学模型 ,以研究传感器的动态性能 ,是动态测试的一个重要内容。研究了递归神经网络模型在传感器动态建模中的应用。递归神经网络模型采用具有输入层、中间层、输出层的三层网络结构 ,整个网络的特性决定于相邻层间的连接权。采用递推预报误差算法训练神经网络 ,具有收敛速度快、收敛精度高的特点。由于其反馈特征 ,使得递归神经网络模型能获取系统的动态响应特性。该方法特别适用于传感器非线性动态建模 ,而且避免了传感器模型阶次的选择的困难。试验结果表明 ,应用递归神经网络对传感器进行动态建模是一种行之有效的方法

Application of Recurrent Neural Network to Dynamic Modeling of Sensors

Dynamic modeling of sensors is an important aspect in the fields of instrument technique.The recurrent neural network is proposed for nonlinear dynamic modeling of sensors.The recurrent neural networks whose structures are determined by the nodes of input and middle layers possess the ability of dynamic mapping.With the feedback behavior,the recurrent neural network can catch up with the dynamic response of the system.A recursive prediction error algorithm,which converges fast,is applied to train the recurrent neural network.Experimental results show that the dynamic modeling method is effective.