Qunxi Zhu

Qunxi Zhu
Fudan University · School of Mathematical Sciences

PhD student

About

14
Publications
2,811
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266
Citations
Additional affiliations
September 2016 - June 2021
Fudan University
Position
  • PhD Student

Publications

Publications (14)
Article
Full-text available
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neu...
Conference Paper
Full-text available
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neu...
Preprint
Full-text available
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridge the connection between deep neural networks and dynamical systems. In this article, we introduce a new sort of continuous-depth neu...
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
A Boolean control network (BCN) is a discrete-time dynamical system whose variables take values from a binary set 0,1. At each time step, each variable of the BCN updates its value simultaneously according to a Boolean function which takes the state and control of the previous time step as its input. Given an ordered pair of states of a BCN, we def...
Article
Full-text available
In this article, we focus on a topic of detecting unstable periodic orbits (UPOs) only based on the time series observed from the nonlinear dynamical system whose explicit model is completely unknown a priori. We articulate a data-driven and model-free method which connects a well-known machine learning technique, the reservoir computing, with a wi...
Preprint
A Boolean control network (BCN) is a discrete-time dynamical system whose variables take values from a binary set $\{0,1\}$. At each time step, each variable of the BCN updates its value simultaneously according to a Boolean function which takes the state and control of the previous time step as its input. Given an ordered pair of states of a BCN,...
Article
In this paper, we focus on the topic of stabilizing the Boolean control network (BCN) by an optimal event-triggered feedback control. By routinely transforming the BCN into its algebraic form, constructing the (reverse) weighted digraph and the hypergraph for the BCN, applying the shortest path algorithm to the hypergraph, we obtain an optimal even...
Article
In this article, we investigate the emergence of tissue dynamics with time delays of diffusion. Such emergent dynamics, describing the tissue homeostasis, usually correspond to particular tissue functions, which are attracting a tremendous amount of attention from both communities of mathematical modeling and systems biology. Specifically, in addit...
Article
In this paper, the controllability and observability of sampled-data Boolean control networks (SDBCNs) are investigated. New phenomena are observed in the study of the controllability and observability of SDBCNs. We routinely convert SDBCNs into linear discrete-time systems by the semi-tensor product of matrices. Necessary and sufficient conditions...
Article
This note presents further results based on the recent paper [J. Liang, H. Chen, and J. Lam, “An improved criterion for controllability of Boolean control networks,” IEEE Trans. Autom. Control, vol. 62, no. 11, pp. 6012-6018, Nov. 2017]. After some optimizations, the conventional method can be more efficient than the method used in the above paper....
Article
In this paper, we study the optimal control problem of Boolean control networks (BCNs). An optimal input-state transfer graph (OISTG) is defined for BCNs with cost in every stage. Optimal controllers are designed to minimize (or maximize) a given cost (or payoff) function over finite and/or infinite time horizon. In finite time horizon, a sufficien...
Article
We show some new results on the observability of Boolean control networks (BCNs). First, to study the observability, we combine two BCNs with the same transition matrix into a new BCN. Then, we propose the concept of a reachable set that results in a given set of initial states, and we derive four additional necessary and sufficient conditions for...

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