关键词: 预测, 贮存可靠性, BP神经网络, 粒子群

Abstract: The storage reliability is an important part of quality monitoring in the military reserve. The scientific and accurate prediction of storage reliability is a necessary requirement for the evaluation of the modern military. For the historical storage data, a prediction model of storage reliability is derived based on period and reliability. A prediction method of storage reliability is provided where BP neural network algorithm is optimized by improved particle swarm optimization algorithm based on evolutionary strategy. Data augmentation can improve the quality and quantity of the samples. The improved PSO algorithm can optimize the initial weights and thresholds in BP neural network, and increase the generalization ability. PSO algorithm has better global search ability, and BP neural network has strong local search ability. The proposed algorithm in this paper can avoid precocious phenomena and improve its convergence speed and its prediction accuracy. The prediction results show that the improved-PSO-BP network method proposed in this paper has better prediction performance than PSO-BP and BP neural networks.

Key words: prediction, storage reliability, BP neural network, particle swarm optimization