An adaptive neuro-fuzzy inference system for forecasting australia’s domestic low cost carrier passenger demand
This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANF...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Vilnius Gediminas Technical University
2015-11-01
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| Series: | Aviation |
| Subjects: | |
| Online Access: | http://journals.vgtu.lt/index.php/Aviation/article/view/2551 |
| _version_ | 1819293864359362560 |
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| author | Panarat Srisaeng Glenn S. Baxter Graham Wild |
| author_facet | Panarat Srisaeng Glenn S. Baxter Graham Wild |
| author_sort | Panarat Srisaeng |
| collection | DOAJ |
| description | This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities. |
| first_indexed | 2024-12-24T04:17:12Z |
| format | Article |
| id | doaj.art-3c6475d71c1545a381b8995922ff8a4e |
| institution | Directory Open Access Journal |
| issn | 1648-7788 1822-4180 |
| language | English |
| last_indexed | 2024-12-24T04:17:12Z |
| publishDate | 2015-11-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Aviation |
| spelling | doaj.art-3c6475d71c1545a381b8995922ff8a4e2022-12-21T17:15:54ZengVilnius Gediminas Technical UniversityAviation1648-77881822-41802015-11-0119310.3846/16487788.2015.11048062551An adaptive neuro-fuzzy inference system for forecasting australia’s domestic low cost carrier passenger demandPanarat Srisaeng0Glenn S. Baxter1Graham Wild2School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, Australia 3001School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, Australia 3001School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, Australia 3001This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.http://journals.vgtu.lt/index.php/Aviation/article/view/2551adaptive neuro-fuzzy inference system (ANFIS)air transportAustraliaforecasting methodslow cost carriers |
| spellingShingle | Panarat Srisaeng Glenn S. Baxter Graham Wild An adaptive neuro-fuzzy inference system for forecasting australia’s domestic low cost carrier passenger demand Aviation adaptive neuro-fuzzy inference system (ANFIS) air transport Australia forecasting methods low cost carriers |
| title | An adaptive neuro-fuzzy inference system for forecasting australia’s domestic low cost carrier passenger demand |
| title_full | An adaptive neuro-fuzzy inference system for forecasting australia’s domestic low cost carrier passenger demand |
| title_fullStr | An adaptive neuro-fuzzy inference system for forecasting australia’s domestic low cost carrier passenger demand |
| title_full_unstemmed | An adaptive neuro-fuzzy inference system for forecasting australia’s domestic low cost carrier passenger demand |
| title_short | An adaptive neuro-fuzzy inference system for forecasting australia’s domestic low cost carrier passenger demand |
| title_sort | adaptive neuro fuzzy inference system for forecasting australia s domestic low cost carrier passenger demand |
| topic | adaptive neuro-fuzzy inference system (ANFIS) air transport Australia forecasting methods low cost carriers |
| url | http://journals.vgtu.lt/index.php/Aviation/article/view/2551 |
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