基于免疫遗传算法改进的BP神经网络在灭火系统电路板故障诊断中的应用

针对装甲车辆灭火系统电路板规模较大,功能日趋多样与完善的同时,其复杂程度也日益提高,故障层次越来越多,故障现象与故障原因的映射关系更加复杂,组合故障频发,传统的故障诊断方法已不能满足灭火系统电路板故障诊断的要求。设计了基于免疫遗传算法优化的BP神经网络对灭火系统电路板进行故障诊断,并在免疫和遗传过程中保留了部分训练最优解。实现了神经网络收敛速度的提高,使用Matlab编程优化算法并完成了电路板仿真故障的诊断。通过实验验证了该诊断模型的准确性和可靠性,为电气系统通用检测设备的神经网络诊断方法实现提供了理论支撑。

The application of BP neural network improved by Genetic immune algorithm in fire extinguishing system circuit board fault diagnosis

In view of the armored vehicle fire extinguishing system circuit board size is bigger, function has become increasingly diverse and perfect at the same time, and the complexity is improved, fault levels become more complex, the relationship of the fault phenomenon and the cause of the problem becomes more complex, combination malfunction appears frequently, the traditional fault diagnosis methods can not meet the requirements of fire extinguishing system circuit board fault diagnosis. The thesis designs immune genetic algorithm to optimize the BP neural network fault diagnosis to fire extinguishing system circuit board, and retained part of the training optimal solution in the process of immune and genetic. The algorithm realize the improvement of the neural network convergence speed, using Matlab programming optimization algorithm and circuit board fault diagnosis simulation is completed. The accuracy and reliability of the diagnosis model is verified by experiment, and the neural network diagnosis method provides a theoretical support for the general testing equipment of electrical systems.