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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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1026-0226 1607-887X |