用于传感器非线性误差校正的新颖神经网络

该文阐述了用神经网络校正传感系统非线性误差的原理和方法,提出了一种新颖的简化小脑模型神经网络（SCMAC）及其模型、算法与实现技术.模型、算法采用直接权地址映射技术,以训练样本的输入为地址,建立起输入与权重的关系.任意输入作为相近的权地址,即可找到对应的权,经过联想插补后可获得高精度输出.此外,采用磁盘文件存储、寻址权重等方法,避免了微机内存溢出,使得实现容易.最后给出了一个仿真实验.实验结果表明,用SCMAC校正后,可使传感器的非线性误差减少到近似为零.

A Novel CMAC Neural Network for Correcting the Sensor's Nonlinear Errors

In this paper, the principles and the methods for correcting the nonlinear errors of the sensor system with a neural network are shown, and a novel simplified cerebella model articulation controller (SCMAC), which includes its model, algorithm and realized techniques, is proposed. The direct weight address mapping techniques are used in this model and algorithm, and the relation between the inputs and the weights is established by taking the inputs of training samples as their weight address, and the corresponding weights are found for any inputs by taking it as similar weight address, and the accurate outputs are obtained by the associable insertion algorithm. In addition, the weights are stored and addressed in a magnetic disk file, therefore, the overflow of internal memory of microcomputer are avoided, and the SCMAC is easily realized. Finally, a simulation experiment is given and the results show that the nonlinear errors of the sensor system are decreased to approximate zero after correcting with a SCMAC.