Finite-Time Stability Criteria for a Class of High-Order Fractional Cohen–Grossberg Neural Networks with Delay
This paper focuses on a class of delayed fractional Cohen–Grossberg neural networks with the fractional order between 1 and 2. Two kinds of criteria are developed to guarantee the finite-time stability of networks based on some analytical techniques. This method is different from those in some earli...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3604738 |
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| _version_ | 1832560033993326592 |
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| author | Zhanying Yang Jie Zhang Junhao Hu Jun Mei |
| author_facet | Zhanying Yang Jie Zhang Junhao Hu Jun Mei |
| author_sort | Zhanying Yang |
| collection | DOAJ |
| description | This paper focuses on a class of delayed fractional Cohen–Grossberg neural networks with the fractional order between 1 and 2. Two kinds of criteria are developed to guarantee the finite-time stability of networks based on some analytical techniques. This method is different from those in some earlier works. Moreover, the obtained criteria are expressed as some algebraic inequalities independent of the Mittag–Leffler functions, and thus, the calculation is relatively simple in both theoretical analysis and practical applications. Finally, the feasibility and validity of obtained results are supported by the analysis of numerical simulations. |
| format | Article |
| id | doaj-art-58a5ad6d81c54991bfbc3dc687ae9c1d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-58a5ad6d81c54991bfbc3dc687ae9c1d2025-02-03T01:28:32ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/36047383604738Finite-Time Stability Criteria for a Class of High-Order Fractional Cohen–Grossberg Neural Networks with DelayZhanying Yang0Jie Zhang1Junhao Hu2Jun Mei3School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, Hubei, ChinaSchool of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, Hubei, ChinaSchool of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, Hubei, ChinaSchool of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, Hubei, ChinaThis paper focuses on a class of delayed fractional Cohen–Grossberg neural networks with the fractional order between 1 and 2. Two kinds of criteria are developed to guarantee the finite-time stability of networks based on some analytical techniques. This method is different from those in some earlier works. Moreover, the obtained criteria are expressed as some algebraic inequalities independent of the Mittag–Leffler functions, and thus, the calculation is relatively simple in both theoretical analysis and practical applications. Finally, the feasibility and validity of obtained results are supported by the analysis of numerical simulations.http://dx.doi.org/10.1155/2020/3604738 |
| spellingShingle | Zhanying Yang Jie Zhang Junhao Hu Jun Mei Finite-Time Stability Criteria for a Class of High-Order Fractional Cohen–Grossberg Neural Networks with Delay Complexity |
| title | Finite-Time Stability Criteria for a Class of High-Order Fractional Cohen–Grossberg Neural Networks with Delay |
| title_full | Finite-Time Stability Criteria for a Class of High-Order Fractional Cohen–Grossberg Neural Networks with Delay |
| title_fullStr | Finite-Time Stability Criteria for a Class of High-Order Fractional Cohen–Grossberg Neural Networks with Delay |
| title_full_unstemmed | Finite-Time Stability Criteria for a Class of High-Order Fractional Cohen–Grossberg Neural Networks with Delay |
| title_short | Finite-Time Stability Criteria for a Class of High-Order Fractional Cohen–Grossberg Neural Networks with Delay |
| title_sort | finite time stability criteria for a class of high order fractional cohen grossberg neural networks with delay |
| url | http://dx.doi.org/10.1155/2020/3604738 |
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