【摘要】 趋势预测是实现旋转机械先进的预知维护的关键技术,神经网络模型预测是实现趋势预测的新途径。当前旋转机械状态预测神经网络对环境的适应性较差、预测精度较低。针对这个问题,提出了一种在线自适应趋势预测方法。利用遗传算法(GA)的并行搜索能力对BP网络结构参数进行动态优化。改进后的预测模型能够根据不同条件对结构参数进行动态优化,取得了较理想的在线预测效果。更多还原

【Abstract】 A trend predicting is a key technology to achieve the advanced predictive maintenance. A prediction of neural network model is a new way to achieve the trend predicting. The present predicting models of neural networks for working conditions prediction to rotary machinery are comparatively poor in adaptability to environment and in accuracy of predicting; In view for the problems, a new way of on-line optimization for trend predicting is put forward. Using the capability of parallel search with genetic algorithm (GA) dynamically optimizes the structure parameters of BP network. The predicting model improved may dynamically optimize the structure parameters according to different conditions. The more satisfactory results of the on-line predicting are gained.更多还原