Predicting the Capacitance of Parallel Plate Capacitors Using Adaptive Neuro-Fuzzy Inference System (ANFIS)
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International Journal of Recent Engineering Science (IJRES) |  |
| © 2020 by IJRES Journal | ||
| Volume-7 Issue-5 |
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| Year of Publication : 2020 | ||
| Authors : Zvikomborero Hweju, Varaidzo Sostina Dandira |
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| DOI : 10.14445/23497157/IJRES-V7I5P105 |
Citation
MLA Style :Zvikomborero Hweju, Varaidzo Sostina Dandira "Predicting the Capacitance of Parallel Plate Capacitors Using Adaptive Neuro-Fuzzy Inference System (ANFIS)" International Journal of Recent Engineering Science 7.5(2020):26-28.
APA Style :Zvikomborero Hweju, Varaidzo Sostina Dandira. Predicting the Capacitance of Parallel Plate Capacitors Using Adaptive Neuro-Fuzzy Inference System (ANFIS) International Journal of Recent Engineering Science, 7(5),26-28.
Abstract
Parallel plate capacitors are indispensable passive devices with diverse applications in the field of electronics. The accurate prediction of capacitance value at full charge is of paramount importance during this valuable device`s design stage. This work presents a parallel plate capacitance prediction using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The design of the experiment has been achieved using the Taguchi experimental design method. This study`s control variables are dielectric absolute permittivity, plate surface area, and plate separation, while capacitance is the single response variable considered. The ANFIS model has a 100% prediction accuracy on training datasets and 83.63% prediction accuracy on testing datasets. The results indicate the reliability of the ANFIS model in parallel plate capacitance prediction during the design stage.
Reference
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Keywords
ANFIS, Parallel Plate Capacitance, Absolute Permittivity, Plate Surface Area, and Plate Separation.