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 | ||||
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