Yao Guo

Yao Guo
Fudan University · School of Mathematical Sciences

PhD

About

10
Publications
2,157
Reads
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70
Citations
Citations since 2016
7 Research Items
62 Citations
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2016201720182019202020212022051015

Publications

Publications (10)
Article
Full-text available
In this paper, we investigate the almost sure stability of switched systems on randomly switching durations simultaneously with randomly switching interaction matrices. We not only allow the interaction matrix on each switching duration to take values randomly from either a countable, an uncountable, or even an unbounded state space, but also allow...
Article
In this paper, we consider a switched system comprising finitely or infinitely many subsystems described by linear time-delayed differential equations and a rule that orches- trates the system switching randomly among these subsystems, where the switching times are also randomly chosen. We first construct a counterintuitive example where even thoug...
Article
Full-text available
In this article, we develop some approaches, which enable us to more accurately and analytically identify the essential patterns that guarantee the almost sure stability of discrete-time systems with random switches. We allow for the case that the elements in the switching connection matrix even obey some unbounded and continuous-valued distributio...
Article
Full-text available
In this article, we investigate the dynamics of non-Bayesian social learning model with periodically switching structures. Unlike the strongly connectedness conditions set for the temporal connecting networks of the non-Bayesian social learning to guarantee its convergence in the literature, our model configurations are essentially relaxed in a man...
Preprint
Full-text available
Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of the original framework. Here...
Conference Paper
Full-text available
Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of the original framework. Here...
Article
Full-text available
In this article, we report a phenomenon of collective dynamics on discrete-time complex networks: random temporal interaction matrix even of zero or/and small average are able to significantly enhance synchronization with probability one. According to current knowledge, there is no verifiably sufficient criterion on what kind of the random temporal...
Data
Full-text available
In this supplementary information, we perform a rigorous and systematic analysis of a transcendental characteristic equation with complex coefficients, thereby establishing necessary and sufficient conditions for guaran- teeing the stability of the controlled supercritical-Hopf-bifurcation model with a time delay feedback. Besides, we expatiate, re...
Article
Full-text available
In this letter, we find how the frequency of an oscillation determines the exact form of the control for suppressing the oscillation through feedback controls with time delays. These results are based on necessary and sufficient conditions we analytically established for the stability of a dynamical system with feedback control and time delays. We...
Article
Full-text available
In this paper, we show that a fast switch is able to lead a complex dynamical system to being asymptotically stable, although this system is completely unstable in every switch duration and even the associated connection matrices are randomly selected. Importantly, we define some new exponents by which we can figure out the essential patterns that...

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