Abstract
This paper presents an application of multilayered feedforward neural networks for controlling a PT326 process trainer laboratory equipment. Firstly, the process as well as its inverse have been identified 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 axe included in a comparative study.
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© 1998 Springer-Verlag Wien
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Ribeiro, B., Cardoso, A. (1998). A Model-based Neural Network Controller for a Process Trainer Laboratory Equipment. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_133
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_133
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
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