Tuning the Parameters of TSK Neuro-Fuzzy System by Particle Swarm Optimization | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 14, Volume 28, Issue 2, July 2019, Page 245-258 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.62781 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Sally Abdulaziz* ; Essam Nabil; Gomaa Zaki; Galal Atlam | ||||
| Dept. of Industrial Elect. and Control Eng., Faculty of Elect., Eng., Menoufia University, Egypt. | ||||
| Abstract | ||||
| Particle Swarm Optimization (PSO) algorithm is applied to improve the efficiency of Takagi-Sugeno-Kang (TSK) neuro-fuzzy network in identification of nonlinear system. First, a TSK type neuro-fuzzy system is adopted for improving identification and prediction, and then PSO technique is adopted to optimize the execution of neuro-fuzzy network. The simulation results indicate that the applied PSO accomplishes good performance and tracks the plant output with minimal error. | ||||
| References | ||||
|
h: 0px; "> [1] Sandhu, Gurpreet S., and Kuldip S. Rattan. "Design of a neuro-fuzzy controller." Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on. Vol. 4. IEEE, 1997. [2] J Pérez, A Gajate, V Milanés, E Onieva and M Santos “Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles”, IEEE International Conference on, 1-6-2010. [3] Boumediene ALLAOUA, Abdellah LAOUFI, Brahim GASBAOUI, and Abdessalam ABDERRAHMANI “Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization” Issue 15, July-December 2009. e-adjust: auto; -webkit-text-stroke-wi[4] C.J. Lin, C.H. Chen and C.Y. Lee, “Efficient immune-based particle swarm optimization learning for neuro-fuzzy networks design”, Journal of Information Science and Engineering, vol.24, no.5, pp.1505-1520,2008. [5] C.J. Lin and S.J. Hong, “The design of neuro-fuzzy networks using particle swarm optimization and recursive singular value decomposition”, Neurocomputing 71 297–310,2007. [6] Z. L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system”. IEEE Trans. on Energy Conversion, vol. 19, Issue: 2, pp. 384-391,2004. [7] C.J. Lin, “An efficient immune-based symbiotic particle swarm optimization learning algorithm for TSK-type neuro-fuzzy network design”, Fuzzy Sets Sys, vol. 159, pp. 2890-2909,2008. [8] C.J.Lin, C.C Peng and C.Y. Lee “Identification and Prediction Using Neuro-Fuzzy Networks with Symbiotic Adaptive Particle Swarm Optimization”. Informatica (Slovenia)35(1): 113-122 ,2011. [9] C.F. Juang, “A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms”, IEEE Trans. on Fuzzy Systems, vol. 10, no. 2, pp. 155-170,2002. [10] J. Kennedy and R. Eberhart , “Particle swarm optimization”Proc. IEEE Int‟l Conf. Neural Networks, pp. 1942-1948,1995. [11] D.P. Rini, S.M. Shamsuddin and S.S. Yuhaniz “Particle swarm optimization: Technique, system and challenges” International Journal of Computer Applications, 14 (1) (2011), pp. 19–26. [12] R. Poli, J. Kennedy, and T. Blackwell. “Particle swarm optimization. An overview. Swarm Intelligence”, 1(1):33-57, 2007. [13] R. Kothandaraman. and L. Ponnusamy., “PSO tuned Adaptive Neuro-fuzzy Controller for Vehicle Suspension Systems,” Journal of Advances in Information Technology, vol. 3, pp. 57-63, Feb 2012. [14] Y. Fukuyama, et al., “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,”IEEE Trans. Power Systems ,vol. 15,no. 4,november 2000 | ||||
|
Statistics Article View: 183 |
||||
