Empirical and feed forward neural networks models of tapioca starch hydrolysis
The aim of dynamic modeling of the tapioca starch hydrolysis process is to generate models for forecasting the future product concentration (glucose) from the initial conditions of available process measurements. This paper compares two methods of modeling the tapioca starch hydrolysis process: (1)...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Taylor & Francis
2006
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/7132/ http://dx.doi.org/10.1080/08839510500191422 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1845471752105230336 |
|---|---|
| author | Rashid, Roslina Jamaluddin, Hishamuddin Saidina Amin, Nor Aishah |
| author_facet | Rashid, Roslina Jamaluddin, Hishamuddin Saidina Amin, Nor Aishah |
| author_sort | Rashid, Roslina |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The aim of dynamic modeling of the tapioca starch hydrolysis process is to generate models for forecasting the future product concentration (glucose) from the initial conditions of available process measurements. This paper compares two methods of modeling the tapioca starch hydrolysis process: (1) The empirical approach and (2) the feed forward neural network (FFNN) approach. Experiments were conducted to obtain a set of data for the modeling purpose. The Gauss-Newton method was used for parameter estimation in the empirical analysis and a multilayer neural network with one hidden layer was utilized in the neural networks approach. This study indicates that the FFNN model of tapioca starch hydrolysis produces better predictive accuracy, that is simpler to develop and has a generalization capability compared with the empirical model. |
| format | Article |
| id | my.utm.eprints-7132 |
| institution | Universiti Teknologi Malaysia |
| publishDate | 2006 |
| publisher | Taylor & Francis |
| record_format | eprints |
| spelling | my.utm.eprints-71322009-07-02T06:47:59Z http://eprints.utm.my/7132/ Empirical and feed forward neural networks models of tapioca starch hydrolysis Rashid, Roslina Jamaluddin, Hishamuddin Saidina Amin, Nor Aishah TJ Mechanical engineering and machinery The aim of dynamic modeling of the tapioca starch hydrolysis process is to generate models for forecasting the future product concentration (glucose) from the initial conditions of available process measurements. This paper compares two methods of modeling the tapioca starch hydrolysis process: (1) The empirical approach and (2) the feed forward neural network (FFNN) approach. Experiments were conducted to obtain a set of data for the modeling purpose. The Gauss-Newton method was used for parameter estimation in the empirical analysis and a multilayer neural network with one hidden layer was utilized in the neural networks approach. This study indicates that the FFNN model of tapioca starch hydrolysis produces better predictive accuracy, that is simpler to develop and has a generalization capability compared with the empirical model. Taylor & Francis 2006-06 Article PeerReviewed Rashid, Roslina and Jamaluddin, Hishamuddin and Saidina Amin, Nor Aishah (2006) Empirical and feed forward neural networks models of tapioca starch hydrolysis. Applied Artificial Intelligence, 20 (1). pp. 79-97. ISSN 0883-9514 http://dx.doi.org/10.1080/08839510500191422 10.1080/08839510500191422 |
| spellingShingle | TJ Mechanical engineering and machinery Rashid, Roslina Jamaluddin, Hishamuddin Saidina Amin, Nor Aishah Empirical and feed forward neural networks models of tapioca starch hydrolysis |
| title | Empirical and feed forward neural networks models of tapioca starch hydrolysis |
| title_full | Empirical and feed forward neural networks models of tapioca starch hydrolysis |
| title_fullStr | Empirical and feed forward neural networks models of tapioca starch hydrolysis |
| title_full_unstemmed | Empirical and feed forward neural networks models of tapioca starch hydrolysis |
| title_short | Empirical and feed forward neural networks models of tapioca starch hydrolysis |
| title_sort | empirical and feed forward neural networks models of tapioca starch hydrolysis |
| topic | TJ Mechanical engineering and machinery |
| url | http://eprints.utm.my/7132/ http://dx.doi.org/10.1080/08839510500191422 |
| url_provider | http://eprints.utm.my/ |