Hierarchical neural fuzzy models as a tool for process identification: a bioprocess application

Meleiro, L.A.C., Filho, R. Maciel, Campello, R.J.D.B., and Amaral, W.C. (2001) Hierarchical neural fuzzy models as a tool for process identification: a bioprocess application. In: Majutaba, I.M., and Hussain, M.A., (eds.) Application of Neural Networks and Other Learning Technologies in Process Engineering. Imperial College Press, London, UK, pp. 173-196.

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Hierarchical structures have been introduced in the literature to deal with the dimensionality problem, which is the main drawback to the application of neural networks and fuzzy models to the modeling and control of large-scale systems. In the present work, hierarchical neural fuzzy models are reviewed focusing on an industrial application. The models considered here consist of a set of Radial Basis Function (RBF) networks formulated as simplified fuzzy systems and connected in a cascade fashion. These models are applied to the modeling of a Multi-Input/Multi-Output (MIMO) complex biotechnological process for ethyl alcohol (ethanol) production and show to adequately describe the dynamics of this process, even for long-range horizon predictions.

Item ID: 47612
Item Type: Book Chapter (Research - B1)
ISBN: 978-1-86094-263-1
Date Deposited: 08 Mar 2017 07:40
FoR Codes: 01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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