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Incorporating fast and intelligent control technique into ecology: A Chebyshev neural network-based terminal sliding mode approach for fractional chaotic ecological systems

机译:将快速和智能控制技术融入生态:基于Chebyshev神经网络的终端滑动模式方法,用于分数混沌生态系统

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摘要

In the present study, a new neural network-based terminal sliding mode technique is proposed to stabilize and synchronize fractional-order chaotic ecological systems in finite-time. The Chebyshev neural network is implemented to estimate unknown functions of the system. Moreover, through the proposed Chebyshev neural network observer, the effects of external disturbances are fully taken into account. The weights of the Chebyshev neural network observer are adjusted based on adaptive laws. The finite-time convergence of the closed-loop system, which is a new concept for ecological systems, is proven. Then, the dependency of the system on the value of the fractional time derivatives is investigated. Lastly, the proposed control scheme is applied to the fractional-order ecological system. Through numerical simulations, the performance of the developed technique for synchronization and stabilization are assessed and compared with a conventional method. The numerical simulations strongly corroborate the effective performance of the proposed control technique in terms of accuracy, robustness, and convergence time for the unknown nonlinear system in the presence of external disturbances.
机译:在本研究中,提出了一种新的基于神经网络的终端滑动模式技术,以在有限时间中稳定和同步分数顺序混沌生态系统。 Chebyshev神经网络被实现为估计系统的未知功能。此外,通过拟议的Chebyshev神经网络观察器,完全考虑了外部干扰的影响。基于自适应法调整了Chebyshev神经网络观察者的权重。闭环系统的有限时间收敛性是生态系统的新概念,是生态系统的新概念。然后,研究了系统对分数时间衍生物的值的依赖性。最后,拟议的控制方案应用于分数阶生态系统。通过数值模拟,评估并与常规方法进行评估和比较开发技术进行同步和稳定化的性能。数值模拟强烈证实了在存在外部干扰存在下未知非线性系统的准确性,鲁棒性和收敛时间方面的有效性能。

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