Ecological Complexity ( IF 3.5 ) Pub Date : 2021-07-13 , DOI: 10.1016/j.ecocom.2021.100943 Bo Wang 1, 2 , Hadi Jahanshahi 3 , Hemen Dutta 4 , Ernesto Zambrano-Serrano 5 , Vladimir Grebenyuk 6 , Stelios Bekiros 7, 8 , Ayman A. Aly 9
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.
中文翻译:
将快速智能控制技术融入生态:一种基于切比雪夫神经网络的分形混沌生态系统终端滑模方法
在本研究中,提出了一种新的基于神经网络的终端滑模技术,以在有限时间内稳定和同步分数阶混沌生态系统。Chebyshev 神经网络用于估计系统的未知函数。此外,通过提出的切比雪夫神经网络观察器,充分考虑了外部干扰的影响。Chebyshev 神经网络观察器的权重根据自适应规律进行调整。证明了闭环系统的有限时间收敛性,这是生态系统的一个新概念。然后,研究了系统对分数时间导数值的依赖性。最后,将所提出的控制方案应用于分数阶生态系统。通过数值模拟,对所开发的同步和稳定技术的性能进行了评估,并与传统方法进行了比较。数值模拟有力地证实了所提出的控制技术在存在外部干扰的情况下在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。