摘要
考虑了模糊系统辨识过程中的不适定问题.系统前件部分采用标准模糊C均值聚类算法对输入空间进行划分,从已知的系统输入数据中提取系统模糊规则.针对后件参数辨识过程中的不适定性问题,采用Tikhonov正则化方法,在最小化泛函中引入正则化泛函来解决整个辨识过程中的不适定.进一步,应用贝叶斯方法来计算正则化参数,并给出了具体算法.仿真结果表明,该方法具有适定性.
We consider the ill-posedness of the fuzzy system identification process. The standard fuzzy c-means clustering algorithm is used to divide the input space, and fuzzy rules are extracted from the known input data in the system. To counteract the ill-posedness in the consequent parameter identification process, we apply the Tikhonov regularization method and introduce the regularized functional in the minimizing functional to solve ill-posed problems. Then we use the Bayesian method to calculate the regularization parameter, and we give the specific algorithm. Simulation resuhs show that this method has well-posedness.
出处
《信息与控制》
CSCD
北大核心
2014年第4期447-450,456,共5页
Information and Control
基金
国家自然科学基金资助项目(61273011
61273003)
