测试技术学报2004，Vol.18Issue(2)：99-103,5.
基于递归神经网络的传感器非线性动态建模
Nonlinear Dynamic Modelling of Sensors Based on Recursive Neural Network
田社平 1丁国清 1颜德田 1石猛1
作者信息
- 1. 上海交通大学,信息检测技术与仪器系,上海,200030
- 折叠
摘要
Abstract
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.关键词
递归神经网络/传感器/非线性动态建模/递归预报误差算法Key words
recursive neural network/sensor/nonlinear dynamic modelling/recursive prediction error algorithm分类
信息技术与安全科学引用本文复制引用
田社平,丁国清,颜德田,石猛..基于递归神经网络的传感器非线性动态建模[J].测试技术学报,2004,18(2):99-103,5.
