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.