Scholarlink

<p>This research explores a novel fractional-order hyperchaotic Dong-Wang system, investigating its dynamic behavior, including stability, periodicity, chaotic transitions, and the effects of varying fractional order and system parameters. The system’s complex dynamics are explored through phase portraits, time-series visualizations, the 0-1 test, Poincaré maps, Lyapunov exponents, and bifurcation diagrams. An adaptive control method based on Lyapunov stability theory is employed to synchronize and stabilize two fractional-order hyperchaotic systems with different initial conditions. The study also assesses the effectiveness of a radial basis function neural network (RBFNN) model in capturing the system’s dynamics. The model’s accuracy is measured using the root mean square error, which provides insights into its predictive capabilities. The results demonstrate the high accuracy and reliability of the RBFNN, as evidenced by its strong resemblance to numerical techniques. This study underscores the potential of RBFNNs to advance computational modeling and control of complex nonlinear dynamics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Analysis, adaptive synchronization and stabilization of fractional-order hyperchaotic dong-wang system with RBF neural network

  • 期刊论文

  • 发表日期：

    2026年3月1日

查看全文
  • M. G. Abbas Malik,
  • Noman Ahmed,
  • Zia Bashir

摘要

This research explores a novel fractional-order hyperchaotic Dong-Wang system, investigating its dynamic behavior, including stability, periodicity, chaotic transitions, and the effects of varying fractional order and system parameters. The system’s complex dynamics are explored through phase portraits, time-series visualizations, the 0-1 test, Poincaré maps, Lyapunov exponents, and bifurcation diagrams. An adaptive control method based on Lyapunov stability theory is employed to synchronize and stabilize two fractional-order hyperchaotic systems with different initial conditions. The study also assesses the effectiveness of a radial basis function neural network (RBFNN) model in capturing the system’s dynamics. The model’s accuracy is measured using the root mean square error, which provides insights into its predictive capabilities. The results demonstrate the high accuracy and reliability of the RBFNN, as evidenced by its strong resemblance to numerical techniques. This study underscores the potential of RBFNNs to advance computational modeling and control of complex nonlinear dynamics.