电子科技大学学报2016，Vol.46Issue(6)：923-928,6.DOI:10.3969/j.issn.1001-0548.2016.06.008
基于离散过程神经网络的装备技术状态预测方法
Equipment’s Condition Prediction Based on the Discrete Process Neural Networks
摘要
Abstract
Conventional forecasting methods cannot systematically analyze the aggregation of space and time in multidimensional parameter analysis. To solve the problem, a prediction method based on discrete process neural networks is proposed in this paper. In order to avoid choosing a local optimal solution during the training of the net, the chaotic particle swarm optimization algorithm is introduced in the process of training. Finally, a case study is presented to illustrate the validity of the proposed method.关键词
混沌粒子群算法/技术状态预测/离散过程神经网络/空间聚合Key words
chaotic particle swarm optimization algorithm/condition prediction/discrete process neural networks/space aggregation分类
机械制造引用本文复制引用
张耀辉,韩小孩,王少华..基于离散过程神经网络的装备技术状态预测方法[J].电子科技大学学报,2016,46(6):923-928,6.基金项目
部级基金 （）
