Multi-description coding-decoding-based control for interval type 2 T-S fuzzy systems with mixed time-delays

Zhang, Sunjie; Yan, Tingli; Xie, Su

doi:10.1016/j.jfranklin.2023.03.007

Cited by 2 publications

(1 citation statement)

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“…In the FCM-based decoding, the error of decoding depends on the parameters involved in the granulation process. In addition, the decoding error is commonly used as a criterion to evaluate the quality of the fuzzy clustering [9][10][11]. One can improve the performance of the fuzzy clustering approaches through reducing the decoding error.…”

Section: Introductionmentioning

confidence: 99%

Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism

Xu,

Liu

et al. 2024

International Journal of Intelligent Systems

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Granular computing (GrC) embraces a spectrum of concepts, methodologies, methods, and applications, which dwells upon information granules and their processing. Fuzzy C-means (FCM) based encoding and decoding (granulation-degranulation) mechanism plays a visible role in granular computing. Fuzzy decoding mechanism, also known as the reconstruction (degranulation) problem, has become an intensively studied category in recent years. This study mainly focuses on the improvement of the fuzzy decoding mechanism, and an augmented version achieved through constructing perturbation matrices of prototypes is put forward. Particle swarm optimization is employed to determine a group of optimal perturbation matrices to optimize the prototype matrix and obtain an optimal partition matrix. A series of experiments are carried out to show the enhancement of the proposed method. The experimental results are consistent with the theoretical analysis and demonstrate that the developed method outperforms the traditional FCM-based decoding mechanism.

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Section: Introductionmentioning

confidence: 99%

Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism

Xu,

Liu

et al. 2024

International Journal of Intelligent Systems

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A control scheme for interval type‐2 T‐S fuzzy systems at constrained bit rates: A coding and decoding scheme

Yan,

Zhang,

2024

Asian Journal of Control

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SummaryThis study focuses on a class of interval type‐2 T‐S fuzzy systems under bit rate constraint(BRC), which can adapt to complex systems more flexibly than the traditional interval type‐1 T‐S fuzzy model. In order to guarantee the security of data transmission, and considering that the channel is affected by the bit rate during transmission, this study introduces a codec scheme in the interval type‐2 T‐S fuzzy system and applies it to the channel from the sensor to the controller. With the support of the codec scheme, this study explores the sufficient conditions to satisfy the data recoverability, and based on the satisfaction of these conditions, the system is proposed to satisfy the sufficient conditions for input‐to‐state stability (ISS) and the controller gain. Through the in‐depth analysis in this paper, this study proposes a control scheme that can fit complex systems more accurately and also confirms the importance of considering bit rate constraints in communication systems. Finally, the paper is validated by simulation with two specific examples, which fully demonstrate the superiority and effectiveness of the proposed control scheme.

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Multi-description coding-decoding-based control for interval type 2 T-S fuzzy systems with mixed time-delays

Cited by 2 publications

References 40 publications

Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism

Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism

A control scheme for interval type‐2 T‐S fuzzy systems at constrained bit rates: A coding and decoding scheme

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