摘要
提出用遗传算法优化的Takagi-Sugeno-Kang(TSK)模糊模型对未知或不确定的混沌动力学系统进行辨识。在辨识未知混沌系统的TSK模型过程中,只需利用未知混沌系统的输出时间序列。首先,采用模糊聚类分析方法从训练数据建立其初始TSK模糊模型。然后采用实数编码的遗传算法对初始模型进行优化设计。同时为防止破坏模糊规则的语义属性,对遗传搜索空间采取了适当的限制。用辨识模型重建吸引子方法定性地评价辨识模型,通过计算辨识模型的Lyapunov指数定量地评价辨识模型的性能。仿真结果表明,该辨识模型能很好地逼近原混沌动力学系统,准确地体现原混沌系统的动力学特性。
This paper presents a fuzzy logic-based approach optimized by genetic algorithms (GA) to identify the unknown or uncertain chaotic dynamic systems. Only output data of the underlying system are required to establish the Takagi-Sugeno-Kang (TSK) fuzzy model. The suggested algorithm is composed of two steps. First fuzzy clustering is applied to obtain a compact initial TSK fuzzy model from the data for training. Then this model is optimized by a real-coded GA subjected to constraints that maintain the semantic properties of the rules. A qualitative evaluation of the identified models is made with the reconstruction of an attractor by the identified models, and a quantitative evaluation of the identified models is made with calculation of the Lyapunov exponents of the identified models, too. Simulations show that the identified models can approach the original chaotic dynamic systems and extract the dynamical characteristics of the original chaotic systems.
出处
《系统仿真学报》
CAS
CSCD
2004年第6期1323-1325,1329,共4页
Journal of System Simulation
