基于递归神经网络的传感器非线性动态建模

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。讨论了递归神经网络模型在传感器动态建模中的应用，给出了递归神经网络模型的结构及相应的训练算法。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。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。

Nonlinear Dynamic Modelling of Sensors Based on Recursive Neural Network

Nonlinear dynamic modelling of sensors is an important aspect in the field of instrument technique. The recursive neural network is proposed for nonlinear dynamic modelling of sensors, as its architecture is determined only by the number of nodes in the input, hidden and output layers. With the feedback behavior, the recursive neural network can catch up with the dynamic response of the system. The recursive neural network which involves dynamic elements and feedback connections has important capabilities that are not found in feedforward networks, such as the ability to store information for later use and higher predicting precision. A recursive prediction error algorithm which converges fast is applied to training the recursive neural network. Experimental results show that the performance of the recursive neural network model conforms to the sensor to be modeled, and the method is not only effective but of high precision.