根据层压式木门生产工艺特点及管理需求,利用射频识别(radio frequency identification,RFID)电子标签作为生产、仓储、物流、安装和售后等环节主要信息载体,构建基于RFID技术的垂直分布式木门制造信息采集与处理模型结构,并提出各功能...根据层压式木门生产工艺特点及管理需求,利用射频识别(radio frequency identification,RFID)电子标签作为生产、仓储、物流、安装和售后等环节主要信息载体,构建基于RFID技术的垂直分布式木门制造信息采集与处理模型结构,并提出各功能模块的实现流程。应用结果证明,该信息采集与处理模型可替代传统二维码系统,解决传统生产过程中存在的二维码易污染、重复贴码、数据可读不可写以及数据信息缺失等问题,木门的生产效率由原来的1.5 min/扇提高至1.0 min/扇,综合管理时间由原来的10~15 min/扇缩短至5 min/扇,实现木门加工、仓储、物流、安装和维护保养等环节的全流程管理。展开更多
Chaos theory was introduced for water quality, prediction, and the model of water quality prediction was established by combining phase space reconstruction theory and BP neural network forecasting method. Through the...Chaos theory was introduced for water quality, prediction, and the model of water quality prediction was established by combining phase space reconstruction theory and BP neural network forecasting method. Through the phase space reconstruction, the one-dimensional water quality time series were mapped to be multi-dimensional sequence, which enriched the spatial information of water quality change and expanded mapping region of training samples of BP neural network. Established model of combining chaos theory and BP neural network were applied to forecast turbidity time series of a certain reservoir. Contrast to BP neural network method, the relative error and the mean squared error of the combined method had all varying degrees of lower. Results indicated the neural network model with chaos theory had the higher prediction accuracy, at the same time, it had better fault-tolerant capability and generalization performance .展开更多
文摘Chaos theory was introduced for water quality, prediction, and the model of water quality prediction was established by combining phase space reconstruction theory and BP neural network forecasting method. Through the phase space reconstruction, the one-dimensional water quality time series were mapped to be multi-dimensional sequence, which enriched the spatial information of water quality change and expanded mapping region of training samples of BP neural network. Established model of combining chaos theory and BP neural network were applied to forecast turbidity time series of a certain reservoir. Contrast to BP neural network method, the relative error and the mean squared error of the combined method had all varying degrees of lower. Results indicated the neural network model with chaos theory had the higher prediction accuracy, at the same time, it had better fault-tolerant capability and generalization performance .
