提出并实现了采用主动温度反馈控制电光晶体温度对剩余幅度调制变化的抑制。
An active temperature feedback method is proposed and demonstrated to suppress the RAM fluctuation by controlling the EOM temperature.
基于线性回归和RBF神经网络构建火道软测量模型，为控制建立温度反馈环节。
A flue temperature soft measurement model based on linear regression and RBF neural network was built to establish temperature feedback control.
未来（温度）上升率的不确定性很大程度上由水汽和云层‘反馈’效果造成，这些是当前研究的课题。
Uncertainties in the future rate of (temperature) rise, stemming largely from the 'feedback' effects on water vapour and clouds, are topics of current research.
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