NDLI: Introducing evolving Takagi–Sugeno method based on local least squares support vector machine models

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Introducing evolving Takagi–Sugeno method based on local least squares support vector machine models

Content Provider	Springer Nature Link
Author	Komijani, Mohammad Lucas, Caro Araabi, Babak Nadjar Kalhor, Ahmad
Copyright Year	2011
Abstract	In this study, an efficient local online identification method based on the evolving Takagi–Sugeno least square support vector machine (eTS-LS-SVM) for nonlinear time series prediction is introduced. As an innovation, this paper has applied the nonlinear models, i.e. local LS-SVM models, as the consequence parts of the fuzzy rules, instead of the linear models used in the conventional evolving TS fuzzy models. In each step, the proposed learning approach includes two phases. The fuzzy rules (rule premise) are first created and updated adaptively based on a sequential clustering technique to obtain the structure of TS model. Then, the parameters of each local LS-SVM model (rule consequence) are recursively updated by deriving a new recursive algorithm (a local decremental and incremental procedure) to minimize the local modelling error and trace the process’s dynamics. Besides, a new learning algorithm based on the recursive gradient-based method is used to adaptively update the meta-parameters of the LS-SVM models. Comparison of the suggested method with some of the previous approaches based on the online prediction of the nonlinear time series has shown that the introduced identification algorithm has a proper performance in terms of learning and generalization abilities while having a lower redundancy.
Starting Page	81
Ending Page	93
Page Count	13
File Format	PDF
ISSN	18686478
Journal	Evolving Systems
Volume Number	3
Issue Number	2
e-ISSN	18686486
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-12-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Evolving Takagi–Sugeno Least square support vector machine Time series prediction Statistical Physics, Dynamical Systems and Complexity Complexity Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation Computer Science Applications

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