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Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts.

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Title: Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts.
Authors: Chen, Yebin1 (AUTHOR), Zhang, Xiaoqing1 (AUTHOR), Yan, Zhilian2 (AUTHOR) zlyan@ahut.edu.cn, Faydasicok, Ozlem1,3 (AUTHOR) kozlem@istanbul.edu.tr, Arik, Sabri4 (AUTHOR)
Source: Journal of the Franklin Institute. Dec2024, Vol. 361 Issue 18, pN.PAG-N.PAG. 1p.
Subjects: Hidden Markov models, Stochastic analysis, Switching systems (Telecommunication), Random variables, Probability theory
Abstract: This paper explores sampled-data control for Markovian switching neural networks (MSNNs) with dynamic output quantization and packet dropouts. The primary goal is to construct a multi-mode, quantized sampled-data controller that ensures stochastic stability and H ∞ disturbance-reduction performance of the closed-loop MSNN. A Bernoulli-distributed random variable with uncertain probability is introduced to characterize the incidence of packet dropouts. To describe potential mode inconsistencies that may occur between the MSNN and controller, an exponential hidden Markov model is employed. Furthermore, the quantizer's dynamic scaling factor is intentionally built as a piecewise function to avoid the potential division-by-zero problem. A sufficient condition for stochastic stability and H ∞ disturbance-reduction performance is proposed, utilizing a mode- and time-dependent Lyapunov-type functional and several stochastic analysis tools. Then, through decoupling nonlinearities, a numerically efficient approach for determining the desired controller gains and parameter range associated with the dynamic scaling factor is developed. In order to facilitate comparisons, the situation with no uncertainty in the probability of packet dropouts is studied, and both analysis and design approaches are offered. Finally, two simulation examples are provided to validate the effectiveness and applicability of the developed approaches. • Quantized Sampled-Data Control for Markovian Switching NNs with Packet Dropouts. • Dynamic Scaling Factor Design for Output-Signal Dynamic Quantizer. • Exponential Hidden Markov Model-Based Multi-Mode Control Scheme. • Simultaneous Determination of Desired Controller Gains and Quantized Parameter Range. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Franklin Institute is the property of Pergamon Press - An Imprint of Elsevier Science 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 181650714
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PubType: Periodical
PubTypeId: serialPeriodical
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  Label: Title
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  Data: Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Yebin%22">Chen, Yebin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaoqing%22">Zhang, Xiaoqing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Zhilian%22">Yan, Zhilian</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zlyan@ahut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Faydasicok%2C+Ozlem%22">Faydasicok, Ozlem</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> kozlem@istanbul.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Arik%2C+Sabri%22">Arik, Sabri</searchLink><relatesTo>4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Franklin+Institute%22">Journal of the Franklin Institute</searchLink>. Dec2024, Vol. 361 Issue 18, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Switching+systems+%28Telecommunication%29%22">Switching systems (Telecommunication)</searchLink><br /><searchLink fieldCode="DE" term="%22Random+variables%22">Random variables</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper explores sampled-data control for Markovian switching neural networks (MSNNs) with dynamic output quantization and packet dropouts. The primary goal is to construct a multi-mode, quantized sampled-data controller that ensures stochastic stability and H ∞ disturbance-reduction performance of the closed-loop MSNN. A Bernoulli-distributed random variable with uncertain probability is introduced to characterize the incidence of packet dropouts. To describe potential mode inconsistencies that may occur between the MSNN and controller, an exponential hidden Markov model is employed. Furthermore, the quantizer's dynamic scaling factor is intentionally built as a piecewise function to avoid the potential division-by-zero problem. A sufficient condition for stochastic stability and H ∞ disturbance-reduction performance is proposed, utilizing a mode- and time-dependent Lyapunov-type functional and several stochastic analysis tools. Then, through decoupling nonlinearities, a numerically efficient approach for determining the desired controller gains and parameter range associated with the dynamic scaling factor is developed. In order to facilitate comparisons, the situation with no uncertainty in the probability of packet dropouts is studied, and both analysis and design approaches are offered. Finally, two simulation examples are provided to validate the effectiveness and applicability of the developed approaches. • Quantized Sampled-Data Control for Markovian Switching NNs with Packet Dropouts. • Dynamic Scaling Factor Design for Output-Signal Dynamic Quantizer. • Exponential Hidden Markov Model-Based Multi-Mode Control Scheme. • Simultaneous Determination of Desired Controller Gains and Quantized Parameter Range. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of the Franklin Institute is the property of Pergamon Press - An Imprint of Elsevier Science 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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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.jfranklin.2024.107252
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      – Code: eng
        Text: English
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        PageCount: 1
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    Subjects:
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        Type: general
      – SubjectFull: Stochastic analysis
        Type: general
      – SubjectFull: Switching systems (Telecommunication)
        Type: general
      – SubjectFull: Random variables
        Type: general
      – SubjectFull: Probability theory
        Type: general
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      – TitleFull: Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts.
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            NameFull: Chen, Yebin
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            NameFull: Zhang, Xiaoqing
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            NameFull: Yan, Zhilian
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            NameFull: Faydasicok, Ozlem
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            NameFull: Arik, Sabri
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            – D: 01
              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
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              Value: 361
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