| 摘要 |
A neural network for adjusting a setpoint in process control replaces a human operator. The neural network operates in three modes: training, operation, and retraining. In operation, the neu- ral network is trained using training input data along with input data. The input data is from the sensor(s) monitoring the process. The input data is used by the neural network to develop output data. The training input data are the setpoint adjustments made by a human operator. The output data is compared with the train- ing input data to produce error data, which is used to adjust the. weights of the neural network so as to train it. After training has been completed, the neural network enters the operation mode. In this mode, the present invention uses the input data to predict out- put data used to adjust the setpoint supplied to the regulatory con- troller. Thus, the operator is effectively replaced. The present in- vention in the retraining mode utilizes new training input data to retrain the neural network by adjusting the weight(s).
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