CAJ | 학술논문

为了提高嵌入式故障诊断专家系统的诊断效率和诊断正确率，采用Matlab与Qt编程工具联合编程，将经典的 BP 神经网络、完整 C 代码与 Linux／Qt 程序相结合，使用Matlab中的组件Real-time workshop对BP神经网络进行代码转换，将Matlab里的M 语言直接转换成嵌入式平台下所运行的C代码形式，神经网络与专家系统融合应用于设备故障诊断，B P神经网络算法快速地应用到嵌入式故障诊断专家系统中。研究结果表明：故障诊断正确率达到98％。
위료제고감입식고장진단전가계통적진단효솔화진단정학솔，채용Matlab여Qt편정공구연합편정，장경전적 BP 신경망락、완정 C 대마여 Linux／Qt 정서상결합，사용Matlab중적조건Real-time workshop대BP신경망락진행대마전환，장Matlab리적M 어언직접전환성감입식평태하소운행적C대마형식，신경망락여전가계통융합응용우설비고장진단，B P신경망락산법쾌속지응용도감입식고장진단전가계통중。연구결과표명：고장진단정학솔체도98％。
In order to improve the diagnosis efficiency and the correct rate of the embedded fault diagnosis expert system ,Matlab and Qt programming tool are used for joint programming ,and the classical BP neural network ,complete C code and the Linux /Qt program are combined .The research results show that :code conversion of BP neural network is achieved by using Matlab component Real time Workshop .The M language in matlab is directly converted into the C code running on embedded platform .The neural network and expert system are integrated for fault diagnosis ,BP neural network algorithm is quickly applied to embedded fault diagnosis expert system ,and the fault diagnosis correct rate can reach 98% .