基于最近邻模糊聚类的T-S模糊辨识方法
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摘要
为提高T-S模糊模型的辨识精度和计算效率,并针对传统聚类算法存在的聚类中心选取问题,提出一种基于最近邻模糊聚类的T-S模糊辨识方法。首先,利用所提的最近邻聚类法降低了传统计算中人为预设聚类初始参数的主观性,并提高了聚类效率,其结果作为模糊c均值算法的初始参数,来实现对模糊规则中前提参数的准确辨识,最后结合稳态卡尔曼滤波算法快速估计规则的后件参数。所提方法的有效性通过典型化工过程pH中和过程的建模得以验证。
In order to improve the identification accuracy of the T-S fuzzy model, and to solve the problem of determining the clustering centers in the traditional clustering algorithms, a Nearest Neighbor Fuzzy Clustering-based(NNFC) T-S fuzzy identification method is proposed. Firstly, the proposed nearest neighbor clustering approach decreases the subjectivity of the artificial presetting for the initial parameters of clustering.And the computation efficiency of clustering is also increased. Furthermore, the result of nearest clustering is afforded for the initial parameters of the Fuzzy c-Means(FCM) algorithm. Thus the premise parameters in the fuzzy rules are identified accurately. Finally, the Stable Kalman Filter(SKF) method is combined with the presented NNFC to estimate the consequent parameters quickly. The effectiveness of proposed method is verified by the classic chemical pH neural process.
In order to improve the identification accuracy of the T-S fuzzy model, and to solve the problem of determining the clustering centers in the traditional clustering algorithms, a Nearest Neighbor Fuzzy Clustering-based(NNFC) T-S fuzzy identification method is proposed. Firstly, the proposed nearest neighbor clustering approach decreases the subjectivity of the artificial presetting for the initial parameters of clustering.And the computation efficiency of clustering is also increased. Furthermore, the result of nearest clustering is afforded for the initial parameters of the Fuzzy c-Means(FCM) algorithm. Thus the premise parameters in the fuzzy rules are identified accurately. Finally, the Stable Kalman Filter(SKF) method is combined with the presented NNFC to estimate the consequent parameters quickly. The effectiveness of proposed method is verified by the classic chemical pH neural process.
引文
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[15]吕游,刘吉臻,杨婷婷.基于非线性偏最小二乘回归的软测量建模及应用[J].高校化学工程学报,2014,28(4):818-824.Lu Y,Liu J Z,Yang T T.Soft-Sensing Model Based on Nonlinear Partial Least Squares Regression and its Application[J].Journal of Chemical Engineering of Chinese Universities,2014,28(4):818-824.
[2]D-O A,Pagan J A,Sanz R,et al.Data-driven Prognostics Using a Combination of Constrained K-Means Clustering,Fuzzy Modeling and LOF-Based Score[J].Neurocomputing,2017,241(7):97-107.
[3]Chou C H,Hsieh S C,Qiu C J.Hybrid Genetic Algorithm and Fuzzy Clustering for Bankruptcy Prediction[M].Elsevier Science Publishers B.V.2017.
[4]Siminski K.Fuzzy Weighted C-Ordered Means Clustering Algorithm[J].Fuzzy Sets and Systems,2017,318(1):1-33.
[5]Xu K-K,Li H-X,Yang H-D.Dual Least Squares Support Vector Machines Based Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes[J].Journal of Process Control,2017,54(3):81-89.
[6]Jebarani Evangeline S,Suresh Kumar S,Jayakumar J.Torque Modeling of Switched Reluctance Motor Using LSSVM-DE[J].Neurocomputing,2016,211(26):117-128.
[7]Kaymak U,Babuska R.Compatible Cluster Merging for Fuzzy Modeling[C].Proceedings of IEEE International Conference on Fuzzy Systems.Yokohama:IEEE Press,1995:897-904.
[8]李柠,李少远.基于满意聚类的多模型建模方法[J].控制理论与应用,2003,20(5):783-787.Li N,Li S Y.Multi-model modeling method based on satisfactory clustering[J].Control Theory&Applications,2003,20(5):783-787.
[9]Reyes-Galaviz O F,Pedrycz Witold,Enhancement of the Classification and Reconstruction Performance of Fuzzy C-Means with Refinements of Prototypes[J].Fuzzy Sets and Systems,2017,318(1):80-99.
[10]Jie L L,Liu W D,Sun Z,et al.Hybrid Fuzzy Clustering Methods Based on Improved Self-Adaptive Cellular Genetic Algorithm and Optimal-Selection-Based Fuzzy C-Means[J].Neurocomputing,2017,249(2):140-156.
[11]Rezaee M R,Lelieveldt B P F,Reiber J H C.A New Cluster Validity Index for the Fuzzy C-Mean[J].Pattern Recognition Letters,1998,19(3-4):237-246.
[12]Frey B J,Dueek D.Clusetring by Passing Messages Between Data Points[J].Science,2007,315(5814):972-976.
[13]Zhuang Y,Yagan O.Information Propagation in Clustered Multilayer Networks[J].IEEE Transactions on Networks Science and Enginerring,2016,3(4):211-224.
[14]Nie J H,Loh A P,Hang C C.Modeling pH Neutralization Processes Using Fuzzy-Neural Approaches[J].Fuzzy Sets and Systems,1996,78(1):5-22.
[15]吕游,刘吉臻,杨婷婷.基于非线性偏最小二乘回归的软测量建模及应用[J].高校化学工程学报,2014,28(4):818-824.Lu Y,Liu J Z,Yang T T.Soft-Sensing Model Based on Nonlinear Partial Least Squares Regression and its Application[J].Journal of Chemical Engineering of Chinese Universities,2014,28(4):818-824.