Sugeno inference system for estimating non-linear characteristics in smart sensor applications
This work exploits Sugeno inference system in estimating the non-linear characteristics values closer to the physical quantities sensors measure. In general, the non-linearity of the sensor is modelled by fixing the input of the sensor to the membership function of the inference system and output re...
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Faculty of Natural Resources, University of Sindh, Jamsoro, Pakistan
2016
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| Online Access: | http://irep.iium.edu.my/55974/ http://irep.iium.edu.my/55974/ http://irep.iium.edu.my/55974/7/55974-Sugeno%20Inference%20System%20for%20Estimating%20Non-linear%20Characteristics%20in%20Smart%20Sensor%20Applications%20.pdf |
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iium-559742017-10-23T06:54:23Z http://irep.iium.edu.my/55974/ Sugeno inference system for estimating non-linear characteristics in smart sensor applications Adam, Ismail Khan, Sheroz Yaacob, Mashkuri Ramli, A.F. Zaharuddin, Z Rahman, A.H,A TK452 Electric apparatus and materials. Electric circuits. Electric networks This work exploits Sugeno inference system in estimating the non-linear characteristics values closer to the physical quantities sensors measure. In general, the non-linearity of the sensor is modelled by fixing the input of the sensor to the membership function of the inference system and output response of the sensor to output constant of the inference system. In addition, the input membership function of the inference system is organized in a way that the middle arm of the first membership is vertically aligned to the left arm of the second membership and so for all of the membership functions. While the relationship of the input and output of the inference system thus the sensor is governed by the if-then-rules. The root-mean-square error of the overall developed system is relatively too small to be ignored in sensitive applications, signifying the closer fit to the actual physical quantity data. The minimal multiplication and addition to compute n number of membership functions is found to be n and 2×n respectively. The promising results offer the method to be applied in programmable smart sensor applications. Faculty of Natural Resources, University of Sindh, Jamsoro, Pakistan 2016 Article PeerReviewed application/pdf en http://irep.iium.edu.my/55974/7/55974-Sugeno%20Inference%20System%20for%20Estimating%20Non-linear%20Characteristics%20in%20Smart%20Sensor%20Applications%20.pdf Adam, Ismail and Khan, Sheroz and Yaacob, Mashkuri and Ramli, A.F. and Zaharuddin, Z and Rahman, A.H,A (2016) Sugeno inference system for estimating non-linear characteristics in smart sensor applications. Sindh University Research Journal (Science Series), 48 (4(D)). pp. 157-160. ISSN 1813-1743 http://sujo.usindh.edu.pk/index.php/SURJ/issue/view/149 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TK452 Electric apparatus and materials. Electric circuits. Electric networks |
| spellingShingle |
TK452 Electric apparatus and materials. Electric circuits. Electric networks Adam, Ismail Khan, Sheroz Yaacob, Mashkuri Ramli, A.F. Zaharuddin, Z Rahman, A.H,A Sugeno inference system for estimating non-linear characteristics in smart sensor applications |
| description |
This work exploits Sugeno inference system in estimating the non-linear characteristics values closer to the physical quantities sensors measure. In general, the non-linearity of the sensor is modelled by fixing the input of the sensor to the membership function of the inference system and output response of the sensor to output constant of the inference system. In addition, the input membership function of the inference system is organized in a way that the middle arm of the first membership is vertically aligned to the left arm of the second membership and so for all of the membership functions. While the relationship of the input and output of the inference system thus the sensor is governed by the if-then-rules. The root-mean-square error of the overall developed system is relatively too small to be ignored in sensitive applications, signifying the closer fit to the actual physical quantity data. The minimal multiplication and addition to compute n number of membership functions is found to be n and 2×n respectively. The promising results offer the method to be applied in programmable smart sensor applications. |
| format |
Article |
| author |
Adam, Ismail Khan, Sheroz Yaacob, Mashkuri Ramli, A.F. Zaharuddin, Z Rahman, A.H,A |
| author_facet |
Adam, Ismail Khan, Sheroz Yaacob, Mashkuri Ramli, A.F. Zaharuddin, Z Rahman, A.H,A |
| author_sort |
Adam, Ismail |
| title |
Sugeno inference system for estimating non-linear characteristics in smart sensor applications |
| title_short |
Sugeno inference system for estimating non-linear characteristics in smart sensor applications |
| title_full |
Sugeno inference system for estimating non-linear characteristics in smart sensor applications |
| title_fullStr |
Sugeno inference system for estimating non-linear characteristics in smart sensor applications |
| title_full_unstemmed |
Sugeno inference system for estimating non-linear characteristics in smart sensor applications |
| title_sort |
sugeno inference system for estimating non-linear characteristics in smart sensor applications |
| publisher |
Faculty of Natural Resources, University of Sindh, Jamsoro, Pakistan |
| publishDate |
2016 |
| url |
http://irep.iium.edu.my/55974/ http://irep.iium.edu.my/55974/ http://irep.iium.edu.my/55974/7/55974-Sugeno%20Inference%20System%20for%20Estimating%20Non-linear%20Characteristics%20in%20Smart%20Sensor%20Applications%20.pdf |
| first_indexed |
2023-09-18T21:18:59Z |
| last_indexed |
2023-09-18T21:18:59Z |
| _version_ |
1777411753811902464 |