Condition Prediction of Complex System 复杂系统状态预测
Combined Condition Prediction 联合工况预测
Pure Condition Prediction 纯工况预测
furnace condition prediction 炉况预报
tracking condition prediction 观测预报
wintering condition prediction 越冬条件预报
A synthetic condition prediction model is presented, using neural network and grey theory together make it possible to predict accurately.
提出了设备运行状态综合预测模型,神经网络和灰色理论的组合应用,提高了状态预测的准确性。
参考来源 - 发电设备运行与维修决策支持系统研究The method estimates the parameters of AR model under the non-solution circumstance of linear equation set and fixes the model order through the Akaike information criterion (AIC). The application has presented its advantages in the operating condition prediction.
再次,本文通过对时间序列ARMA、MA和AR模型的机理研究,提出了基于广义逆矩阵最小二乘估计AR模型参数的方法,该方法解决了AR建模中线性方程组无解情况下的参数估计问题,进而根据阿凯克信息论准则确定模型阶次,可成功应用于机械设备运行过程的状态预测。
参考来源 - 基于阶比跟踪和AR模型的旋转机械故障诊断与状态预测技术研究·2,447,543篇论文数据，部分数据来源于NoteExpress
A synthetic condition prediction model is presented, using neural network and grey theory together make it possible to predict accurately.
提出了设备运行状态综合预测模型，神经网络和灰色理论的组合应用，提高了状态预测的准确性。
Based on statistical learning theory (SLT), the relevant problems of solving the machinery intelligent diagnosis and condition prediction are thoroughly researched in this project by means of SVM.
本项目以统计学习理论为基础，深入研究了应用支持向量机方法解决机械智能诊断和状态预测的相关问题。
These researches overcome the shortcoming of needing many fault data samples of existing intelligent diagnosis methods, and provide a new method for machinery fault diagnosis and condition prediction.
本项目的研究克服了传统的智能诊断需要大量故障数据样本的不足，为机械故障诊断和状态预测提供了一种新方法。
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