数学
外稃(植物学)
分数阶微积分
理论(学习稳定性)
订单(交换)
应用数学
控制理论(社会学)
人工神经网络
非线性系统
计算机科学
控制(管理)
物理
禾本科
量子力学
生物
财务
机器学习
人工智能
经济
生态学
作者
Padmaja Narasimman,Balasubramaniam Pagavathi Gounder
出处
期刊:International Journal of Nonlinear Sciences and Numerical Simulation
[De Gruyter]
日期:2022-10-17
被引量:1
标识
DOI:10.1515/ijnsns-2021-0447
摘要
Abstract A detailed survey of existing works on fractional-order nonlinear systems reveals the fact that practically no results exist on stability or any performance analysis of Markovian jumping fractional-order systems (FOSs) in general. The main reason is the theory of infinitesimal generator used to estimate the derivative of Lyapunov–Krasovskii Functional (LKF) is not well-developed in the fractional domain. This shortage, in theory, is focussed in this manuscript. In this work, we provide a lemma that aids in analyzing the stability of fractional-order delayed systems via integer-order derivative of LKF. Using this lemma, by constructing a new suitable LKF and employing known integral inequalities, linear matrix inequality (LMI)-based sufficient conditions that ensure stability along with H ∞ /passive performance of the proposed fractional-order neural networks (FONNs) with Markovian jumping parameters are derived for the first time. Unlike the existing works, the results derived in the present study depend on the fractional order (FO) of the NNs. The importance of such order-dependent criteria is highlighted in numerical examples. Finally, the simulation results are given to show the reliability of the derived conditions.
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