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Bulanık Çıkarım Sisteminde Kural Tahmini için Yeni Yaklaşım: Toplu Taşıma Bakım Sistemi İçin Bir Örnek Olay Çalışması

Year 2020, Volume: 8 Issue: 4, 906 - 915, 01.12.2020
https://doi.org/10.36306/konjes.669505

Abstract

Büyük şehirlerde yaşanan nüfus artışı ve bireysel araç kullanımının artması, trafik problemini de beraberinde getirmiştir. Bu durumların neticesinde toplu taşıma sistemlerinin de olumsuz etkilendiği söylenebilir. Bu anlamda toplu taşıma sistemlerini (TTS) analiz etmek, hem şehir hayatı hem de toplu taşıma kullanıcıları için oldukça kritik ve önemlidir. Toplu taşıma sistemlerinde meydana gelebilecek herhangi bir arızanın birçok sorunu beraberinde getireceği söylenebilir. Günlük hayatın aksaması, can ve mal kayıpları ya da çevreye verilen zarar bu sorunlardan yalnızca birkaçıdır. Bu kapsamda, toplu taşıma sistemleri için etkili bir bakım planlama yapılması çok önemlidir. Bu çalışmada, toplu taşıma sistemlerinin bakım planlamasında, birçok faktörü göz önüne alan bir bulanık tabanlı kural sistemi (BKTS) ile kuralların tahmini ele alınmıştır. Metrobüs sisteminin bakım planlamasından kullanılacak kural tabanlı bu sistem, toplu taşıma sistemlerinde olan arızalar ve bu arızalar karşısında alınacak aksiyonların öngörülmesinde oldukça etkili olacaktır. Bu sistem için önerilen kural tahmini ile bakım prosedürlerinin kesinliğinin ve esnekliğinin arttırılması amaçlanmaktadır. Bu kapsamda, çalışma kapsamında yapay sinir ağları (YSA) geliştirilmiş ve kural tahmini için kullanılmıştır. Bu amaçla, kural tabanında yer almayan on adet durum için tahminleme yapılarak ilgili girdiler için bulanık kural tabanlı bakım çıkarım sisteminin hangi sonuçları ortaya koyduğu belirlenmiştir. Böylece, YSA'nın mevcut kural tabanlı bakım sistemine dahil olmayan kuralların analizi için etkili bir şekilde kullanılabileceği gösterilmiştir.

References

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  • Bhargava, Neeraj, Sakshi Sharma, Renuka Purohit, and Pramod Singh Rathore. 2018. “Prediction of Recurrence Cancer Using J48 Algorithm.” In Proceedings of the 2nd International Conference on Communication and Electronics Systems, ICCES 2017, Institute of Electrical and Electronics Engineers Inc., 386–90.
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  • Gumus, A. T.; Gunerı, A. F. 2009. “A Neural Network Based Demand Forecasting System For Two-Echelon Supply Chains.” In 13th International Research/Expert Conference ”Trends in the Development of Machinery and Associated Technology,.
  • Guneri, Ali Fuat, and Alev Taskin Gumus. 2008. “The Usage of Artificial Neural Networks For Finite Capacity Planning.” International Journal of Industrial Engineering 15(1): 16–25. http://journals.sfu.ca/ijietap/index.php/ijie/article/viewFile/58/30 (December 2, 2018).
  • Kialashaki, Arash, and John R. Reisel. 2014. “Development and Validation of Artificial Neural Network Models of the Energy Demand in the Industrial Sector of the United States.” Energy 76: 749–60. https://www.sciencedirect.com/science/article/abs/pii/S0360544214010263 (December 2, 2018).
  • Kiranyaz, Serkan, Turker Ince, Alper Yildirim, and Moncef Gabbouj. 2009. “Evolutionary Artificial Neural Networks by Multi-Dimensional Particle Swarm Optimization.” Neural Networks 22(10): 1448–62. https://www.sciencedirect.com/science/article/pii/S0893608009001038 (December 2, 2018).
  • Kumar, Paras, S.P. Nigam, and Narotam Kumar. 2014. “Vehicular Traffic Noise Modeling Using Artificial Neural Network Approach.” Transportation Research Part C: Emerging Technologies 40: 111–22. https://www.sciencedirect.com/science/article/pii/S0968090X14000102 (December 2, 2018).
  • Şeker, Şadi Evren. 2015. Weka Ile Veri Madenciliği. İstanbul: Bilgisayar Kavramları Yayınları.
  • Witten, I. H. (Ian H.), Eibe Frank, and Mark A. (Mark Andrew) Hall. 2011. Data Mining : Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
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A NEW APPROACH FOR RULE ESTIMATION OF FUZZY INFERENCE SYSTEM: A CASE STUDY FOR PUBLIC TRANSPORT MAINTENANCE SYSTEM

Year 2020, Volume: 8 Issue: 4, 906 - 915, 01.12.2020
https://doi.org/10.36306/konjes.669505

Abstract

The increase in the population and the high amount of individual vehicle usage in the big cities brought traffic congestion and environmental problems. Additionally, these issues have also some negative effects on the public transport systems (PTSs). In this respect, the analysis of PTS is critical and important for both city life and people. It is possible that the failures in PTS can lead to many problems.
Disruption of daily life, loss of lives and property or damage to the environment are only just a few of these problems. In this context, effective maintenance planning for PTSs is so crucial. In this study, the rule estimation for a fuzzy rule-based system (FRBS) which takes into consideration many factors for the maintenance planning of PTSs is discussed. The rule-based system for maintenance planning of Bus Rapid Transit System (BRT) will be highly effective for the prediction of failures for PTSs and the correct actions to be taken. Rule estimation for this system is aimed to increase the precision and flexibility of maintenance procedures. In this context, a model based on artificial neural networks (ANNs) has been developed and used in rule estimation for FRBS. For this aim, ten cases that are not in the rule base system are estimated and the results of the fuzzy rule-based maintenance inference system for the relevant inputs are revealed.
Thus, it has been shown that ANNs can be used effectively for the analysis of rules that are not included in the current rule-based maintenance system.

References

  • Aljawarneh, Shadi, Muneer Bani Yassein, and Mohammed Aljundi. 2019. “An Enhanced J48 Classification Algorithm for the Anomaly Intrusion Detection Systems.” Cluster Computing 22(5): 10549–65. https://link.springer.com/article/10.1007/s10586-017-1109-8 (June 22, 2020).
  • Avci, Mutlu, and Tulay Yildirim. 2006. “Neural Network Based MOS Transistor Geometry Decision for TSMC 0.18μ Process Technology.” In Springer, Berlin, Heidelberg, 615–22. http://link.springer.com/10.1007/11758549_84 (June 24, 2019).
  • Bhargava, Neeraj, Sakshi Sharma, Renuka Purohit, and Pramod Singh Rathore. 2018. “Prediction of Recurrence Cancer Using J48 Algorithm.” In Proceedings of the 2nd International Conference on Communication and Electronics Systems, ICCES 2017, Institute of Electrical and Electronics Engineers Inc., 386–90.
  • Erdoğan, M. 2018. “The Recommendation of a Decision Support System for Maintenance Management: An Application for the Public Transportation Process.” Yıldız Technical University. https://tez.yok.gov.tr/UlusalTezMerkezi/tezSorguSonucYeni.jsp.
  • Gumus, A. T.; Gunerı, A. F. 2009. “A Neural Network Based Demand Forecasting System For Two-Echelon Supply Chains.” In 13th International Research/Expert Conference ”Trends in the Development of Machinery and Associated Technology,.
  • Guneri, Ali Fuat, and Alev Taskin Gumus. 2008. “The Usage of Artificial Neural Networks For Finite Capacity Planning.” International Journal of Industrial Engineering 15(1): 16–25. http://journals.sfu.ca/ijietap/index.php/ijie/article/viewFile/58/30 (December 2, 2018).
  • Kialashaki, Arash, and John R. Reisel. 2014. “Development and Validation of Artificial Neural Network Models of the Energy Demand in the Industrial Sector of the United States.” Energy 76: 749–60. https://www.sciencedirect.com/science/article/abs/pii/S0360544214010263 (December 2, 2018).
  • Kiranyaz, Serkan, Turker Ince, Alper Yildirim, and Moncef Gabbouj. 2009. “Evolutionary Artificial Neural Networks by Multi-Dimensional Particle Swarm Optimization.” Neural Networks 22(10): 1448–62. https://www.sciencedirect.com/science/article/pii/S0893608009001038 (December 2, 2018).
  • Kumar, Paras, S.P. Nigam, and Narotam Kumar. 2014. “Vehicular Traffic Noise Modeling Using Artificial Neural Network Approach.” Transportation Research Part C: Emerging Technologies 40: 111–22. https://www.sciencedirect.com/science/article/pii/S0968090X14000102 (December 2, 2018).
  • Şeker, Şadi Evren. 2015. Weka Ile Veri Madenciliği. İstanbul: Bilgisayar Kavramları Yayınları.
  • Witten, I. H. (Ian H.), Eibe Frank, and Mark A. (Mark Andrew) Hall. 2011. Data Mining : Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
  • Yadav, Amit Kumar, and S. S. Chandel. 2015. “Solar Energy Potential Assessment of Western Himalayan Indian State of Himachal Pradesh Using J48 Algorithm of WEKA in ANN Based Prediction Model.” Renewable Energy 75: 675–93.
There are 12 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Melike Erdoğan

İhsan Kaya

Publication Date December 1, 2020
Submission Date January 2, 2020
Acceptance Date August 24, 2020
Published in Issue Year 2020 Volume: 8 Issue: 4

Cite

IEEE M. Erdoğan and İ. Kaya, “A NEW APPROACH FOR RULE ESTIMATION OF FUZZY INFERENCE SYSTEM: A CASE STUDY FOR PUBLIC TRANSPORT MAINTENANCE SYSTEM”, KONJES, vol. 8, no. 4, pp. 906–915, 2020, doi: 10.36306/konjes.669505.