Identification of Takagi-Sugeno Model with Self-gravitational Search Algorithm

Authors

Ao Yuan, College of Computing & Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
Xueming Ding, College of Computing & Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;

Abstract

Abstract: An approach of Self-Gravitational Search Algorithm (SGSA) was proposed for the identification of T-S fuzzy model. In the identification of T-S model, structure parameters and consequent parameters were encoded into a particle of SGSA. Based on new strategies, all the individuals in SGSA dynamically adjusted the distance and force between particles according to the intensity of the swarm, and the gravitational constant was adaptively alerted. As a result, the global search ability of the proposed SGSA was enhanced in the earlier stage of the search process to prevent the algorithm from trapping into local regions, in the mean while; the local search capability was improved in the latter iterations of optimization to reduce the probability of the algorithm fruitlessly searching around the local optima. The simulation results and comparative analysis demonstrate the good performance of the proposed method with high accuracy and great robustness.

Recommended Citation

Yuan, Ao and Ding, Xueming (2020) "Identification of Takagi-Sugeno Model with Self-gravitational Search Algorithm," Journal of System Simulation: Vol. 29: Iss. 3, Article 3.
DOI: 10.16182/j.issn1004731x.joss.201703003
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss3/3

First Page

487

Revised Date

2015-08-30

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201703003

Last Page

493

CLC

TP237

Recommended Citation

Ao Yuan, Ding Xueming. Identification of Takagi-Sugeno Model with Self-gravitational Search Algorithm[J]. Journal of System Simulation, 2017, 29(3): 487-493.

DOI

10.16182/j.issn1004731x.joss.201703003