基于RBF神经网络的虚拟仪器测试系统动态补偿方法  

Method for Virtual Instrument Dynamic Compensation of Measurement System Based on RBF Neural Network

作　　者：王亚伟[1] 王中宇[1] 

机构地区：[1]北京航空航天大学仪器科学与光电工程学院,北京100191

出　　处：《微电子学与计算机》2012年第4期76-79,共4页Microelectronics & Computer

基　　金：国家自然科学基金项目(50675011)

摘　　要：测试系统存在着动态测试误差,为了准确地复现出被测量的原始信号,提出了基于RBF神经网络的虚拟仪器测试系统动态补偿方法.该方法不依赖于测试系统的数学模型,而是根据测试系统的输入和响应数据,利用神经网络的强非线性逼近能力获得补偿系统的模型参数,通过LabVIEW构造出测试系统的动态补偿系统.实验结果表明,将RBF神经网络和虚拟仪器相结合,对测试系统进行动态补偿具有良好的效果.Dynamic measurement error always exists in the measurement system, so this paper proposes a method for virtual instrument dynamic compensation of measurement system based on RBF neural network to eliminate the dynamic measurement error and reappear the primitive input signal which is measured accurately. This method uses the strong nonlinearity approximation ability of neural network to obtain the compensating system model parameters according to the measurement systemrs input and response data, and constructs the dynamic compensating system of measurement system through LabVIEW, which does not rely on the mathematical model of measurement systerm The test result indicates that it is effective to carry on the dynamic compensation of measurement system by unifying the RBF neural network and the virtual instrHment_

关 键 词：动态补偿 测试系统 反演 RBF神经网络 虚拟仪器 

分 类 号：TP216[自动化与计算机技术—检测技术与自动化装置]