plc how to realize neural network control

PLC to realize the basic method of neural network control is as follows:

1. in the external software to design and train the neural network model, such as in Matlab with the neural network toolbox to build and train the network.

2. Use tools to convert the trained neural network model into a format that can be imported by PLC, such as C code or function libraries.

3. Import the converted neural network function library in the PLC, and call the neural network prediction function interface to realize control.

4. Call the neural network prediction function according to the input data in the PLC logic to get the output result.

5. According to the neural network output prediction results, in the PLC logic to realize the corresponding control action.

6. Iterative upgrading of the neural network model is achieved by retraining the network in external software and importing the updated model into the PLC.

7. It is also possible to implement the neural network algorithm directly in the PLC through function blocks, but it is more complicated.

Overall, using a hybrid programming model, training the network in external software, and encapsulating the model as a PLC-callable function to realize control, can be more flexible to incorporate the ability of neural network prediction in the PLC.