In the communication equipment fault diagnosis,BP neural network is usually used.But it struggles to swiftly and efficiently obtain the right number of network layers and neurons,as well as having a slow learning rate,which leads to low detec-tion efficiency and poor stability.In order to improve the fault diagnosis efficiency of a certain type of communication equipment,this paper introduces a new optimization algorithm-Sparrow Search Algorithm(SSA)to improve the selection of BP neural network pa-rameters,optimize the parameter settings,and improve the detection speed on the basis of BP neural network as the diagnostic algo-rithm.On contrast with the traditional BP neural network algorithm,the BP neural network fault detection based on sparrow search algorithm has obvious advantages in diagnosis efficiency,which provides a good guidance for further equipment maintenance and provides an algorithm basis for the development of subsequent relevant diagnosis software platform.
维普
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