Lyapunov functions for nabla discrete fractional order systems
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Abstract (arXiv)
This paper focuses on the fractional difference of Lyapunov functions related to Riemann-Liouville, Caputo and Grunwald-Letnikov definitions. A new way of building Lyapunov functions is introduced and then five inequalities are derived for each definition. With the help of the developed inequalities, the sufficient conditions can be obtained to guarantee the asymptotic stability of the nabla discrete fractional order nonlinear systems. Finally, three illustrative examples are presented to demonstrate the validity and feasibility of the proposed theoretical results.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Dec 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 0; 2025: 2.
6 papers.
- 2021A Caputo fractional derivative-based algorithm for optimizationcited by 3
- 2023The Novel Adaptive Fractional Order Gradient Decent Algorithms Design via Robust Controlcited by 2
- 2025Introducing Fractional Classification Loss for Robust Learning with Noisy Labelscited by 2
- 2021Performance Analysis of Fractional Learning Algorithmscited by 1
- 2023Convergence Analysis of Fractional Gradient Descentcited by 0
- 2025Enhancing Fractional Gradient Descent with Learned Optimizerscited by 0
- 2025Enhancing Fractional Gradient Descent with Learned Optimizers21 Oct 2025
- 2025Introducing Fractional Classification Loss for Robust Learning with Noisy Labels8 Aug 2025
- 2023Convergence Analysis of Fractional Gradient Descent30 Nov 2023
- 2023The Novel Adaptive Fractional Order Gradient Decent Algorithms Design via Robust Control8 Mar 2023
- 2021Performance Analysis of Fractional Learning Algorithms11 Oct 2021
- 2021A Caputo fractional derivative-based algorithm for optimization6 Apr 2021