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。

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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。

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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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