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\cal H\infty Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems.

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Title: \cal H\infty Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems.
Authors: Su, Xiaojie, Wu, Ligang, Shi, Peng, Song, Yong-Duan
Source: IEEE Transactions on Systems, Man & Cybernetics: Part B; Dec2012, Vol. 42 Issue 6, p1574-1585, 12p
Subject Terms: FUZZY systems, STOCHASTIC systems, MEAN square algorithms, MATHEMATICAL optimization, ALGORITHMS
Abstract: This paper is concerned with the problem of \cal H\infty model reduction for Takagi–Sugeno (T–S) fuzzy stochastic systems. For a given mean-square stable T–S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an \cal H\infty performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Transactions on Systems, Man & Cybernetics: Part B is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: \cal H\infty Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems.
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  Data: IEEE Transactions on Systems, Man & Cybernetics: Part B; Dec2012, Vol. 42 Issue 6, p1574-1585, 12p
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  Data: This paper is concerned with the problem of \cal H\infty model reduction for Takagi–Sugeno (T–S) fuzzy stochastic systems. For a given mean-square stable T–S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an \cal H\infty performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Systems, Man & Cybernetics: Part B is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TSMCB.2012.2195723
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        Text: English
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    Subjects:
      – SubjectFull: FUZZY systems
        Type: general
      – SubjectFull: STOCHASTIC systems
        Type: general
      – SubjectFull: MEAN square algorithms
        Type: general
      – SubjectFull: MATHEMATICAL optimization
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      – SubjectFull: ALGORITHMS
        Type: general
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      – TitleFull: \cal H\infty Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems.
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            NameFull: Su, Xiaojie
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            NameFull: Wu, Ligang
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            NameFull: Shi, Peng
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              M: 12
              Text: Dec2012
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              Y: 2012
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