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Adaptive Synchronization of Fractional-Order Delayed Memristive Neural Networks

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Recent Advances in Control Problems of Dynamical Systems and Networks

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 301))

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Abstract

In this chapter, an adaptive synchronization problem is considered for fractional-order memristor-based neural networks (FMNNs) with time delay. Adaptive delay feedback control and a fractional-order inequality are adopted to get synchronization between the master system and the slave system. Novel synchronization criteria are constructed. Ultimately, numerical simulations are carried out to testify the main results.

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Acknowledgements

The work of H. Bao was jointly supported by the National Natural Science Foundation of China under Grant No. 61203096, the Chinese Postdoctoral Science Foundation under Grant 2013M513924, the Fundamental Research Funds for Central Universities XDJK2013C001 and the scientific research support project for teachers with doctor’s degree, Southwest University under Grant No. SWU112024. The work of J.H. Park was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2B5B02002002).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Southwest University, Chongqing, 400715, People’s Republic of China

    Haibo Bao

  2. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, 38541, Republic of Korea

    Ju H. Park

  3. School of Mathematics, Southeast University, Nanjing, 210096, People’s Republic of China

    Jinde Cao

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  1. Haibo Bao
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  2. Ju H. Park
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  3. Jinde Cao
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Correspondence to Ju H. Park .

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  1. Department of Electrical Engineering, Yeungnam University, Kyongsan, Korea (Republic of)

    Ju H. Park

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Bao, H., Park, J.H., Cao, J. (2021). Adaptive Synchronization of Fractional-Order Delayed Memristive Neural Networks. In: Park, J. (eds) Recent Advances in Control Problems of Dynamical Systems and Networks. Studies in Systems, Decision and Control, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-49123-9_13

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