摘要
在总结非线性建模经验的基础上,给出了一种建立精确且运行速度快的T-S模型的方法.首先,为了提高运行速度,用聚类的方法减少模糊规则的数目,并确定每个规则中数据的数目.然后,通过回归最小二乘法初步确定T-S模型规则中的状态矩阵的参数.最后,通过梯度下降方法,精确确定T-S模糊模型的所有参数.仿真实例证明了此方法的有效性.
An approach for building accurate and fast running T-S models is proposed based on summarization of the experience of nonlinear modeling. Firstly, the number of fuzzy rules is reduced with clustering algorithm in order to improve the running speed of the model, and the number of data in each rule is determined. Then, the parameters of the state matrixes in the rules of T-S model are determined initially with regressive least squares algorithm. Finally, all of the parameters of the T-S model are determined accurately with gradient descent algorithm. The simulation with a real example shows the effectiveness of the approach.
出处
《信息与控制》
CSCD
北大核心
2006年第1期6-11,共6页
Information and Control
基金
国家自然科学基金资助项目(60404011
60372085)
关键词
梯度下降法
建模
非线性系统
T-S模糊模型
gradient descent algorithm
modeling
nonlinear system
T-S fuzzy model
