摘要
提出一种基于自适应引力搜索算法(Self-Gravitational Search Algorithm,SGSA)的T-S模型辨识方法,把T-S模型的前件参数和后件参数编码进一个粒子中用SGSA辨识。SGSA是针对标准引力搜索算法(GSA)收敛过快的缺点,在GSA的基础上,根据群体密集程度动态调整粒子间的距离和受力大小,并自适应修改引力常数G的改进引力搜索算法。不仅增加了算法在前期的全局搜索能力,防止其过早收敛;而且降低了算法在后期最优解附近震荡的影响,提高了算法的开采能力。仿真结果表明该方法能获得较高的辨识精度,验证了算法的有效性。
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.
作者
敖媛
丁学明
Ao Yuan Ding Xueming(College of Computing ＆ Electrical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, Chin)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2017年第3期487-493,共7页
Journal of System Simulation
基金
宝山区科技创新专项资金(bkw201408)
沪江基金(C14002)
