本文用双隐层BP人工神经网络建立了丝杆螺母副表面边界膜温度特性的磨损自补偿数学模型。
The BP neural network used in the temperature characteristic of the boundary film on the screw-nut pairs surface in the wear-self-compensation system was established.
用BP人工神经网络建立了重载丝杆螺母副的摩擦学特性与载荷之间关系的磨损自补偿数学模型。
The BP neural network used the wear-self-compensation mathematics model of the relationship of tribology characteristic with load of the heavy load screw-nut pairs was established.
文中针对一种高速运算放大器ad713的自激进行了测试，并且通过相位滞后补偿网络消除自激振荡，工作稳定。
As high speed operation amplifier AD713 is example, self-excited oscillation is tested, phase compensate network is offered and the result is stable.
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