System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field
Stochastic disturbances often occur in real-world systems which can lead to undesirable system dynamics. Therefore, it is necessary to investigate stochastic disturbances in neural network modeling. As such, this paper examines the stability problem for Takagi-Sugeno fuzzy uncertain quaternion-value...
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AIMS Press
2023-03-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2023587?viewType=HTML |
| _version_ | 1797856497037737984 |
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| author | R. Sriraman R. Samidurai V. C. Amritha G. Rachakit Prasanalakshmi Balaji |
| author_facet | R. Sriraman R. Samidurai V. C. Amritha G. Rachakit Prasanalakshmi Balaji |
| author_sort | R. Sriraman |
| collection | DOAJ |
| description | Stochastic disturbances often occur in real-world systems which can lead to undesirable system dynamics. Therefore, it is necessary to investigate stochastic disturbances in neural network modeling. As such, this paper examines the stability problem for Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks. By applying Takagi-Sugeno fuzzy models and stochastic analysis, we first consider a general form of Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks with time-varying delays. Then, by constructing suitable Lyapunov-Krasovskii functional, we present new delay-dependent robust and global asymptotic stability criteria for the considered networks. Furthermore, we present our results in terms of real-valued linear matrix inequalities that can be solved in MATLAB LMI toolbox. Finally, two numerical examples are presented with their simulations to demonstrate the validity of the theoretical analysis. |
| first_indexed | 2024-04-09T20:41:23Z |
| format | Article |
| id | doaj.art-9e801958c5fe40fc8f1208950ee960bf |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-04-09T20:41:23Z |
| publishDate | 2023-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-9e801958c5fe40fc8f1208950ee960bf2023-03-30T01:15:16ZengAIMS PressAIMS Mathematics2473-69882023-03-0185115891161610.3934/math.2023587System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion fieldR. Sriraman0R. Samidurai1V. C. Amritha2G. Rachakit3Prasanalakshmi Balaji 41. Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu-603 203, India2. Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu-632 115, India3. Department of Mathematics, National Institute of Technology Warangal, Telangana-506004, India4. Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai-50290, Thailand5. Department of Computer Science, King Khalid University, Abha-62529, Saudi ArabiaStochastic disturbances often occur in real-world systems which can lead to undesirable system dynamics. Therefore, it is necessary to investigate stochastic disturbances in neural network modeling. As such, this paper examines the stability problem for Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks. By applying Takagi-Sugeno fuzzy models and stochastic analysis, we first consider a general form of Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks with time-varying delays. Then, by constructing suitable Lyapunov-Krasovskii functional, we present new delay-dependent robust and global asymptotic stability criteria for the considered networks. Furthermore, we present our results in terms of real-valued linear matrix inequalities that can be solved in MATLAB LMI toolbox. Finally, two numerical examples are presented with their simulations to demonstrate the validity of the theoretical analysis.https://www.aimspress.com/article/doi/10.3934/math.2023587?viewType=HTMLquaternion-valued neural networksrobust stabilitystochastic disturbancelyapunov-krasovskii functionaltakagi-sugeno fuzzy |
| spellingShingle | R. Sriraman R. Samidurai V. C. Amritha G. Rachakit Prasanalakshmi Balaji System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field AIMS Mathematics quaternion-valued neural networks robust stability stochastic disturbance lyapunov-krasovskii functional takagi-sugeno fuzzy |
| title | System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field |
| title_full | System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field |
| title_fullStr | System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field |
| title_full_unstemmed | System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field |
| title_short | System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field |
| title_sort | system decomposition based stability criteria for takagi sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field |
| topic | quaternion-valued neural networks robust stability stochastic disturbance lyapunov-krasovskii functional takagi-sugeno fuzzy |
| url | https://www.aimspress.com/article/doi/10.3934/math.2023587?viewType=HTML |
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