Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays
In this paper, the global O(t-α) synchronization problem is investigated for a class of fractional-order neural networks with time delays. Taking into account both better control performance and energy saving, we make the first attempt to introduce centralized data-sampling approach to characterize...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2017/6157292 |
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| author | Jin-E Zhang |
| author_facet | Jin-E Zhang |
| author_sort | Jin-E Zhang |
| collection | DOAJ |
| description | In this paper, the global O(t-α) synchronization problem is investigated for a class of fractional-order neural networks with time delays. Taking into account both better control performance and energy saving, we make the first attempt to introduce centralized data-sampling approach to characterize the O(t-α) synchronization design strategy. A sufficient criterion is given under which the drive-response-based coupled neural networks can achieve global O(t-α) synchronization. It is worth noting that, by using centralized data-sampling principle, fractional-order Lyapunov-like technique, and fractional-order Leibniz rule, the designed controller performs very well. Two numerical examples are presented to illustrate the efficiency of the proposed centralized data-sampling scheme. |
| format | Article |
| id | doaj-art-7c998cab0ed141d5abfc6ef287ad9d4b |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-7c998cab0ed141d5abfc6ef287ad9d4b2025-08-20T02:07:24ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/61572926157292Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time DelaysJin-E Zhang0Hubei Normal University, Hubei 435002, ChinaIn this paper, the global O(t-α) synchronization problem is investigated for a class of fractional-order neural networks with time delays. Taking into account both better control performance and energy saving, we make the first attempt to introduce centralized data-sampling approach to characterize the O(t-α) synchronization design strategy. A sufficient criterion is given under which the drive-response-based coupled neural networks can achieve global O(t-α) synchronization. It is worth noting that, by using centralized data-sampling principle, fractional-order Lyapunov-like technique, and fractional-order Leibniz rule, the designed controller performs very well. Two numerical examples are presented to illustrate the efficiency of the proposed centralized data-sampling scheme.http://dx.doi.org/10.1155/2017/6157292 |
| spellingShingle | Jin-E Zhang Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays Discrete Dynamics in Nature and Society |
| title | Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays |
| title_full | Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays |
| title_fullStr | Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays |
| title_full_unstemmed | Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays |
| title_short | Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays |
| title_sort | centralized data sampling approach for global ot α synchronization of fractional order neural networks with time delays |
| url | http://dx.doi.org/10.1155/2017/6157292 |
| work_keys_str_mv | AT jinezhang centralizeddatasamplingapproachforglobalotasynchronizationoffractionalorderneuralnetworkswithtimedelays |