Title: LMI Conditions for Global Stability of Fractional-Order Neural Networks
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| Authors: Zhang, S; Yu, YG; Yu, JZ
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| Author Full Names: Zhang, Shuo; Yu, Yongguang; Yu, Junzhi
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| Source: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 28 (10):2423-2433; 10.1109/TNNLS.2016.2574842 OCT 2017
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| Language: English
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| Abstract: Fractional-order neural networks play a vital role in modeling the information processing of neuronal interactions. It is still an open and necessary topic for fractional-order neural networks to investigate their global stability. This paper proposes some simplified linear matrix inequality (LMI) stability conditions for fractional-order linear and nonlinear systems. Then, the global stability analysis of fractional-order neural networks employs the results from the obtained LMI conditions. In the LMI form, the obtained results include the existence and uniqueness of equilibrium point and its global stability, which simplify and extend some previous work on the stability analysis of the fractional-order neural networks. Moreover, a generalized projective synchronization method between such neural systems is given, along with its corresponding LMI condition. Finally, two numerical examples are provided to illustrate the effectiveness of the established LMI conditions.
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| ISSN: 2162-237X
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| eISSN: 2162-2388
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| IDS Number: FH6NH
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| Unique ID: WOS:000411293200017
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| PubMed ID: 27529877
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