Using identification of neural networks, a new method of robust iterative learning control algorithm is proposed in the paper.
在神经网络辨识的基础上，提出一种新的鲁棒迭代学习控制方法。
Simulation results of contrapose Bioreactor show that the proposed method can accelerate learning process and is robust to larger parameter changes.
生化反应器定值控制的仿真结果表明，该方法加快了学习过程，并对更大范围的参数变化具有鲁棒性。
As a result, the method is faster in learning speed, high in accuracy and robust in noise resistance, it is more suitable for sensor's correction and temperature compensation.
因此，该学习速度快、补偿精度高、抗噪声干扰能力强，适合传感器温度补偿及校正。
As a result, the method is faster in learning speed, high in accuracy and robust in noise resistance, it is more suitable for sensor's correction and temperature compensation.
因此，该学习速度快、补偿精度高、抗噪声干扰能力强，适合传感器温度补偿及校正。
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