Abstract
Artificial neural networks have been recognized as a valuable framework for nonlinear identification and control. In this paper we discuss and compare the use of two types of neural network arquitectures (1) MNN (Multilayer Neural Network) and (2) RBFNN (Radial Basis Function Neural Network) for modelling a second order nonlinear chemical process — a lime kiln in the pulp and paper industry. The simulation results showed that MNN performs better in this practical case. Therefore, it was used in an IMC (Internal Model Control) strategy. The neurocontroller was analysed with regards to performance and robustness against disturbances.
Partially supported by Junta Nacional de Investigação Científica (JNICT) and PORTUCEL.
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Ribeiro, B., Dourado, A., Costa, E. (1995). Industrial Kiln Multivariable Control: MNN and RBFNN Approaches. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_106
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_106
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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