| 摘要 |
The neural network process control system is configured with five neural process models Ä1-5Ü, each of which has four inputs for signals from different process parameter sensor Ä6-9Ü outputs. Each model has an output fed to a common weighting stage Ä10Ü that provides an output to a regulator Ä11Ü coupled to the process. The regulator can be a fuzzy controller. The process neural models are trained with data sets and the weighting module determines a standard variation value relative to an average value. This is compared with a defined limit and if exceeded an alarm Ä12Ü is activated. |
