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Mathematical Problems in Engineering
Volume 2013, Article ID 595639, 10 pages
http://dx.doi.org/10.1155/2013/595639
Research Article

Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm

1School of Digital Media, Jiangnan University, Wuxi, Jiangsu 214122, China
2Department of Software, Wuxi Institute of Technology, Wuxi, Jiangsu 214122, China
3Department of Information Technology, China Ship Science Research Centre, Wuxi 214082, China
4Key Laboratory of Advanced Control for Light Industry (Ministry of Education, China), Jiangnan University, Wuxi, Jiangsu 214122, China

Received 30 March 2013; Accepted 23 May 2013

Academic Editor: Yi-Kuei Lin

Copyright © 2013 Peilin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [13 citations]

The following is the list of published articles that have cited the current article.

  • Kashif Hussain, and Mohd Najib Mohd Salleh, “Optimization of fuzzy neural network using APSO for predicting strength of Malaysian SMEs,” 2015 10th Asian Control Conference (ASCC), pp. 1–6, . View at Publisher · View at Google Scholar
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  • Dervis Karaboga, and Ebubekir Kaya, “An adaptive and hybrid artificial bee colony algorithm (aABC) for ANFIS training,” Applied Soft Computing Journal, vol. 49, pp. 423–436, 2016. View at Publisher · View at Google Scholar
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  • Mohd Najib Mohd Salleh, and Kashif Hussain, “Accelerated mine blast algorithm for ANFIS training for solving classification problems,” International Journal of Software Engineering and its Applications, vol. 10, no. 6, pp. 161–168, 2016. View at Publisher · View at Google Scholar
  • Abdul Aziz Abdul Raman, Shaliza Ibrahim, Baharak Sajjadi, and Perumal Asaithambi, “Hybrid nero-fuzzy methods for estimation of ultrasound and mechanically stirring Influences on biodiesel synthesis through transesterification,” Measurement: Journal of the International Measurement Confederation, vol. 103, pp. 62–76, 2017. View at Publisher · View at Google Scholar
  • Erik Cuevas, Primitivo D?az, Omar Avalos, Daniel Zald?var, and Marco P?rez-Cisneros, “Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm,” Applied Intelligence, 2017. View at Publisher · View at Google Scholar
  • Roslina, Muhammad Zarlis, Iwan Tri Riyadi Yanto, and Dedy Hartama, “A framework of training ANFIS using Chicken Swarm Optimization for solving classification problems,” 2016 International Conference on Informatics and Computing, ICIC 2016, pp. 437–441, 2017. View at Publisher · View at Google Scholar
  • Reda Elbarougy, and Masato Akagi, “Optimizing fuzzy inference systems for improving speech emotion recognition,” Advances in Intelligent Systems and Computing, vol. 533, pp. 85–95, 2017. View at Publisher · View at Google Scholar
  • Mohd. Najib Mohd. Salleh, Kashif Hussain, Jamal Uddin, and Rashid Naseem, “Optimization of ANFIS using artificial bee colony algorithm for classification of Malaysian SMEs,” Advances in Intelligent Systems and Computing, vol. 549, pp. 21–30, 2017. View at Publisher · View at Google Scholar
  • Dervis Karaboga, and Ebubekir Kaya, “Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey,” Artificial Intelligence Review, 2018. View at Publisher · View at Google Scholar