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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 [8 citations]

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

  • Weishang Gao, Cheng Shao, and Yi An, “Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization,” Mathematical Problems in Engineering, vol. 2013, pp. 1–13, 2013. View at Publisher · View at Google Scholar
  • Ahmad Bagheri, Hamed Mohammadi Peyhani, and Mohsen Akbari, “Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization,” Expert Systems with Applications, vol. 41, no. 14, pp. 6235–6250, 2014. View at Publisher · View at Google Scholar
  • Zahra Beheshti, Siti Mariyam Shamsuddin, and Sarina Sulaiman, “Fusion Global-Local-Topology Particle Swarm Optimization for Global Optimization Problems,” Mathematical Problems in Engineering, vol. 2014, pp. 1–19, 2014. View at Publisher · View at Google Scholar
  • 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
  • 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
  • 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