Identification of essential oil extraction system using Radial Basis Function (RBF) Neural Network
This paper presents an application of the Radial Basis Function Neural Network (RBFNN)-based identification of an essential oil extraction using Non-Linear Autoregressive Model with Exogenous Inputs (NARX) model. The dataset consisted of a Pseudo-Random Binary Sequence (PRBS) inputs as the control s...
| Published in: | Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, CSPA 2012 |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
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2012
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861537546&doi=10.1109%2fCSPA.2012.6194779&partnerID=40&md5=63cd12214de8b1ceb4d809f763079d11 |
| Summary: | This paper presents an application of the Radial Basis Function Neural Network (RBFNN)-based identification of an essential oil extraction using Non-Linear Autoregressive Model with Exogenous Inputs (NARX) model. The dataset consisted of a Pseudo-Random Binary Sequence (PRBS) inputs as the control signal, and outputs depicting temperatures inside the distillation column. One Step Ahead (OSA) model fitting and residual tests demonstrated that the RBFNN-based NARX model was able to approximate the system well, while satisfying all validation criterias. © 2012 IEEE. |
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| DOI: | 10.1109/CSPA.2012.6194779 |