Exponentially anti‐synchronization control of fuzzy quaternion‐valued memristive neural networks: Matrix measure strategies and Frobenius norm methods.
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| Title: | Exponentially anti‐synchronization control of fuzzy quaternion‐valued memristive neural networks: Matrix measure strategies and Frobenius norm methods. |
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| Authors: | Si, Linli1 (AUTHOR), Li, Ruoxia1 (AUTHOR) ruoxiali1227@163.com |
| Source: | Asian Journal of Control. Mar2026, Vol. 28 Issue 2, p782-791. 10p. |
| Subjects: | Exponential stability, Artificial neural networks, Mathematical analysis, Dynamical systems, Synchronization |
| Abstract: | This study proposes a non‐decomposition method to examine the anti‐synchronization control of quaternion‐valued memristive neural networks with fuzzy terms. Using nonlinear scalarization method, the entire analysis does not use reduced order conversion, nor does it involve the separation of real and imaginary parts, but directly focuses on the original system, which preserved the integrity of the quaternion‐valued system. Furthermore, by means of the definition of x,x∈ℚ$$ \sqrt{x},x\in \mathrm{\mathbb{Q}} $$ , the quaternion‐valued memristive system is translated into a robust system with uncertain terms, which advanced some existing conclusions. Subsequently, sufficient conditions are derived to ensure the error system is exponential stable. Finally, examples are presented to demonstrate the proposed results. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This study proposes a non‐decomposition method to examine the anti‐synchronization control of quaternion‐valued memristive neural networks with fuzzy terms. Using nonlinear scalarization method, the entire analysis does not use reduced order conversion, nor does it involve the separation of real and imaginary parts, but directly focuses on the original system, which preserved the integrity of the quaternion‐valued system. Furthermore, by means of the definition of x,x∈ℚ$$ \sqrt{x},x\in \mathrm{\mathbb{Q}} $$ , the quaternion‐valued memristive system is translated into a robust system with uncertain terms, which advanced some existing conclusions. Subsequently, sufficient conditions are derived to ensure the error system is exponential stable. Finally, examples are presented to demonstrate the proposed results. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 15618625 |
| DOI: | 10.1002/asjc.3689 |
