中国物理B ›› 2017, Vol. 26 ›› Issue (3): 30504-030504.doi: 10.1088/1674-1056/26/3/030504

• GENERAL • 上一篇    下一篇

Adaptive fuzzy synchronization for a class of fractional-order neural networks

Heng Liu(刘恒), Sheng-Gang Li(李生刚), Hong-Xing Wang(王宏兴), Guan-Jun Li(李冠军)   

  1. 1 College of Mathematics and Information Science, Shaanxi Normal Universtiy, Xi'an 710119, China;
    2 Department of Applied Mathematics, Huainan Normal University, Huainan 232038, China
  • 收稿日期:2016-05-12 修回日期:2016-12-04 出版日期:2017-03-05 发布日期:2017-03-05
  • 通讯作者: Sheng-Gang Li E-mail:shengganglinew@126.com
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 11401243 and 61403157), the Foundation for Distinguished Young Talents in Higher Education of Anhui Province, China (Grant No. GXYQZD2016257), the Fundamental Research Funds for the Central Universities of China (Grant No. GK201504002), the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China (Grant Nos. KJ2015A256 and KJ2016A665), the Natural Science Foundation of Anhui Province, China (Grant No. 1508085QA16), and the Innovation Funds of Graduate Programs of Shaanxi Normal University, China (Grant No. 2015CXB008).

Adaptive fuzzy synchronization for a class of fractional-order neural networks

Heng Liu(刘恒)1,2, Sheng-Gang Li(李生刚)1, Hong-Xing Wang(王宏兴)2, Guan-Jun Li(李冠军)2   

  1. 1 College of Mathematics and Information Science, Shaanxi Normal Universtiy, Xi'an 710119, China;
    2 Department of Applied Mathematics, Huainan Normal University, Huainan 232038, China
  • Received:2016-05-12 Revised:2016-12-04 Online:2017-03-05 Published:2017-03-05
  • Contact: Sheng-Gang Li E-mail:shengganglinew@126.com
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 11401243 and 61403157), the Foundation for Distinguished Young Talents in Higher Education of Anhui Province, China (Grant No. GXYQZD2016257), the Fundamental Research Funds for the Central Universities of China (Grant No. GK201504002), the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China (Grant Nos. KJ2015A256 and KJ2016A665), the Natural Science Foundation of Anhui Province, China (Grant No. 1508085QA16), and the Innovation Funds of Graduate Programs of Shaanxi Normal University, China (Grant No. 2015CXB008).

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摘要： In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as synchronization errors, are employed to approximate the unknown nonlinear functions. Based on the fractional Lyapunov stability criterion, an adaptive fuzzy synchronization controller is designed, and the stability of the closed-loop system, the convergence of the synchronization error, as well as the boundedness of all signals involved can be guaranteed. To update the fuzzy parameters, fractional-order adaptations laws are proposed. Just like the stability analysis in integer-order systems, a quadratic Lyapunov function is used in this paper. Finally, simulation examples are given to show the effectiveness of the proposed method.

关键词: fractional-order neural network, adaptive fuzzy control, fractional-order adaptation law

Abstract: In this paper, synchronization for a class of uncertain fractional-order neural networks with external disturbances is discussed by means of adaptive fuzzy control. Fuzzy logic systems, whose inputs are chosen as synchronization errors, are employed to approximate the unknown nonlinear functions. Based on the fractional Lyapunov stability criterion, an adaptive fuzzy synchronization controller is designed, and the stability of the closed-loop system, the convergence of the synchronization error, as well as the boundedness of all signals involved can be guaranteed. To update the fuzzy parameters, fractional-order adaptations laws are proposed. Just like the stability analysis in integer-order systems, a quadratic Lyapunov function is used in this paper. Finally, simulation examples are given to show the effectiveness of the proposed method.

Key words: fractional-order neural network, adaptive fuzzy control, fractional-order adaptation law

中图分类号:  (Control of chaos, applications of chaos)

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