提高传感器输出特性线性化的神经网络方法

Neural Network Methods for Improving Linear Regression of Sensor Output Characteristic

采用BP神经网络结构和LM网络学习规则对传感器系统的非线性误差进行线性校正和补偿 ,并对网络结构进行了适当的改进。用Matlab语言实现 ,计算比较了使用两种不同初始化规则和网络结构时对网络性能的影响。计算机仿真实验表明使用NW初始化规则并改进网络结构后 ,网络的收敛速度更快 ,拟合精度也有所改善。

BP neutral network structure and LM study rule of neutral network are applied to compensate and adjust sensor′s nonlinear errors. Training programs are done by MATLAB language. Two different initial rules and network architectures are compared in the aspect of network performance, especially of the converging speed. The results of computer simulation illustrate that the method which uses Nguyen Widrow initial rule and improved network architecture has a high converging speed and good precision.