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Application of Simulation and Neural Network in Radar Fault Diagnosis

机译:仿真与神经网络在雷达故障诊断中的应用

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A scheme for radar fault diagnosis is proposed. It is based on artificial neural networks whose learning sample comes from the Pspice simulation. For single circuit, single layer artificial neural networks are used. Aiming at the problems of slow rate of convergence and falling easily into part minimums in BP algorithm, a new improved genetic BP algorithm was put forward. To determine whether the network fall into part minimum point, a discriminant of part minimum was put forth in the training process of neural network. Genetic algorithm was used to revise the weights of the neural network if the BP algorithm fell into minimums. For large and complex system multiplayer artificial neural networks are used, as well as preprocessing layer and postprocessing layer. It can raise diagnosis precision, shorten to train time, raise fault tolerance and the development of fault diagnosis, effective carry out fault diagnosis. A circuit fault of certain radar are diagnosed using the simulation and neural network scheme put forward in this paper, which testified the validity of this diagnosis process.
机译:提出了一种雷达故障诊断计划。它基于人工神经网络,其学习样本来自PSPICE仿真。对于单电路,使用单层人工神经网络。针对BP算法中慢速收敛速率缓慢和落下的问题,提出了一种新的改进的遗传BP算法。为了确定网络是否分为最小点,在神经网络的培训过程中提出了部分最小值的判别。如果BP算法落入最小值,则使用遗传算法来修改神经网络的权重。对于大型和复杂的系统多人多人来说,使用人工神经网络,以及预处理层和后处理层。它可以提高诊断精度,缩短培训时间,提高容错和断层诊断的发展,有效进行故障诊断。使用本文提出的仿真和神经网络方案诊断了某些雷达的电路故障,该方案证明了该诊断过程的有效性。

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