贝叶斯L2型TSK模糊系统

发布时间：2018-04-23 12:28

  本文选题：贝叶斯 + L型TSK模糊系统　； 参考：《控制与决策》2017年10期

【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.
[Abstract]:In order to solve the problem of high time cost for traditional Takagi-Sugeno-Kanko fuzzy system to deal with large-scale data, a modeling strategy of L2 type TSK fuzzy system based on probabilistic model framework is proposed, and a Bayesian L2 type TSK fuzzy system with large scale data processing capability is established. Specifically, based on the probabilistic representation of the output error of L2 type TSK fuzzy system, the parameters of the front and rear parts of the system are jointly studied to improve the generalization ability of the system. In addition, the prior distribution function of Delikley is introduced to sparse representation of fuzzy membership degree, which can compress the sample and reduce the operation time. Experimental results on simulated and real data sets verify the advantages of the proposed fuzzy system.
【作者单位】： 江南大学数字媒体学院;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;
【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)
【分类号】：O159;O212.8
，

本文编号：1791978