论文标题
具有2型学生-TMEMBESHIP函数的贝叶斯方法用于T-S模型识别
A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification
论文作者
论文摘要
事实证明，聚集技术对高吉族人（T-S）模型识别非常成功。基于类型2 fuzzyset的内部，fuzzyc-回归聚类已在非SPARSE数据库上显示出了显着的结果，其性能在稀疏数据上降低了。在本文中，介绍了Fuzzyc-回归模型的原始架构，并是一种新型的基于学生 - 分布的成员资格功能，设计用于稀疏数据建模。为了避免过度拟合，我们采用了贝叶斯方法将阿格斯先验纳入回归系数。我们的方法的其他新颖性在于类型还原，其中最终输出使用Karnik Mendel算法进行计算，并且使用随机渐变的方法优化了模型的结果参数。作为详细的实验，结果示出了提出的方法在标准数据集对各种最新方法的投入都优于表现。
Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno (T-S) fuzzy model identification. Inparticular, fuzzyc-regression clustering based on type-2 fuzzyset has been shown the remarkable results on non-sparse databut their performance degraded on sparse data. In this paper, aninnovative architecture for fuzzyc-regression model is presentedand a novel student-tdistribution based membership functionis designed for sparse data modelling. To avoid the overfitting,we have adopted a Bayesian approach for incorporating aGaussian prior on the regression coefficients. Additional noveltyof our approach lies in type-reduction where the final output iscomputed using Karnik Mendel algorithm and the consequentparameters of the model are optimized using Stochastic GradientDescent method. As detailed experimentation, the result showsthat proposed approach outperforms on standard datasets incomparison of various state-of-the-art methods.
