针对现有时间序列在线预测方法存在对数据特性变化感知与预测及时性不足的问题,创新设计了一种基于信息感知权重与误差预测的时间序列在线预测方法。该方法利用信息感知权重替换代价函数中遗忘因子λ0参量;通过建立输入数据与预测误差...针对现有时间序列在线预测方法存在对数据特性变化感知与预测及时性不足的问题,创新设计了一种基于信息感知权重与误差预测的时间序列在线预测方法。该方法利用信息感知权重替换代价函数中遗忘因子λ0参量;通过建立输入数据与预测误差的映射关系进行误差预测,采用加权误差补偿系数实现误差补偿。通过改变隐含层节点数方法进行多次单步预测实验,实验结果从预测精度和泛化性等多方面验证了设计方法优异的单步预测能力。其中,Sinc、Mackey-Glass和Solar Energy 3个数据选取点的单步预测方差分别达到1.56×10-13、2.29×10-7与1.43。根据实际失效情况分别设定失效电压为5.8与5.6 V,并针对封装降压电源模块加速寿命实验实测数据进行多步预测。五步与十步预测结果显示设计方法均有效预测电源失效。实验结果全面说明设计方法在预测数据特性发生变化情况时,能够稳定、精准且有效地完成在线单步与多步预测。展开更多
A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling...A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.展开更多
文摘针对现有时间序列在线预测方法存在对数据特性变化感知与预测及时性不足的问题,创新设计了一种基于信息感知权重与误差预测的时间序列在线预测方法。该方法利用信息感知权重替换代价函数中遗忘因子λ0参量;通过建立输入数据与预测误差的映射关系进行误差预测,采用加权误差补偿系数实现误差补偿。通过改变隐含层节点数方法进行多次单步预测实验,实验结果从预测精度和泛化性等多方面验证了设计方法优异的单步预测能力。其中,Sinc、Mackey-Glass和Solar Energy 3个数据选取点的单步预测方差分别达到1.56×10-13、2.29×10-7与1.43。根据实际失效情况分别设定失效电压为5.8与5.6 V,并针对封装降压电源模块加速寿命实验实测数据进行多步预测。五步与十步预测结果显示设计方法均有效预测电源失效。实验结果全面说明设计方法在预测数据特性发生变化情况时,能够稳定、精准且有效地完成在线单步与多步预测。
基金the Korea Research Foundation Grant Funded by the Korean Government (MOEHRD) (KRF-2007-331-D00089) Funded by Seoul Development Institute (CS070160)
文摘A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.
