A Model-based Neural Network Controller for a Process Trainer
نویسندگان
چکیده
This paper presents an application of multilayered feed-forward neural networks for controlling a PT326 Process Trainer laboratory equipment. Firstly, the process as well as its inverse have been identi ed using the Levenberg-Marquardt algorithm for neural network training. Secondly an internal model control (IMC) strategy has been used for neurocontrol. Different architectures and learning methods have been investigated for model approximation. Control of the process has been implemented in real-time using the Simulink/Matlab environment. Experimental results regarding the performance of the control scheme are included in a comparative study.
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تاریخ انتشار 1999