贝叶斯L2型TSK模糊系统  

Bayesian L2-norm-Takagi-Sugeno-Kang fuzzy system

作　　者：杭文龙 梁爽[2] 刘解放[1] 王士同[1] HANG Wen-long LIANG Shuang LIU Jie-fang WANG Shi-tong(School ofDigitalMedia, JiangnanUniversity, Wuxi214122, China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

机构地区：[1]江南大学数字媒体学院,江苏无锡214122 [2]中国科学院深圳先进技术研究院广东省机器视觉与虚拟现实技术重点实验室,广东深圳518055

出　　处：《控制与决策》2017年第10期1871-1878,共8页Control and Decision

基　　金：国家自然科学基金项目(61300151;61305097);深圳市基础学科布局项目(JCYJ20160429190300857)

摘　　要：针对传统Takagi-Sugeno-Kan(TSK)模糊系统处理大规模数据时间代价较高的问题,提出一种基于概率模型框架的L2型TSK模糊系统建模策略,建立具有处理大规模数据能力的贝叶斯L2型TSK模糊系统(B-TSK-FS).具体地,基于L2型TSK模糊系统的输出误差概率化表示,对系统前后件参数联合学习,提高系统的泛化能力.另外,引入狄利克雷先验分布函数,对模糊隶属度稀疏化表示,实现样本的压缩,降低运算时间.在模拟和真实数据集上的实验结果验证了所提出模糊系统的优势.Classical Takagi-Sugeno-Kang（TSK） fuzzy systems take too much training time on the large scale datasets. To overcome this difficulty, the new TSK fuzzy system in probability modeling framework, i.e., Baysian L2-Norm-TakagiSugeno-Kang fuzzy system（B-TSK-FS）, is proposed. The B-TSK-FS has the ability to handle large datasets. Specifically,based on the probabilistic output error of the L2-Norm TSK-FS, the B-TSK-FS has better generalization abilities through taking both antecedents and consequents of fuzzy rules into consideration simultaneously. In addition, the Dirichlet prior distribution is introduced to the B-TSK-FS, which makes the fuzzy memberships be represented sparsely, so as to condense the large scale datasets for reducing the running time. The experimental results on the synthetic and real-world datasets show the advantage of the proposed method.

关 键 词：贝叶斯 L2型TSK模糊系统 大规模数据 狄利克雷先验分布 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]