Z
Zhixing Cao
Researcher at East China University of Science and Technology
Publications - 49
Citations - 1022
Zhixing Cao is an academic researcher from East China University of Science and Technology. The author has contributed to research in topics: Iterative learning control & Model predictive control. The author has an hindex of 16, co-authored 41 publications receiving 655 citations. Previous affiliations of Zhixing Cao include Harvard University & University of Edinburgh.
Papers
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Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells
Zhixing Cao,Ramon Grima +1 more
TL;DR: The classical two-state model of stochastic mRNA dynamics in eukaryotic cells is extended to include a considerable number of salient features of single-cell biology, such as cell division, replication, mRNA maturation, dosage compensation, and growth-dependent transcription, and derive expressions for the approximate time-dependent protein-number distributions.
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Linear mapping approximation of gene regulatory networks with stochastic dynamics
Zhixing Cao,Ramon Grima +1 more
TL;DR: A linear-mapping approximation is presented that maps systems with protein–promoter interactions onto approximately equivalent systems with no binding reactions, giving approximate but accurate analytic or semi- analytic solutions for a wide range of model GRNs.
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Nonlinear Monotonically Convergent Iterative Learning Control for Batch Processes
TL;DR: The proposed NMC-ILC is an optimization-based control strategy, in which the original nonlinear process is linearly approximated to reduce the complexity of the optimization problem accompanied.
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Neural network aided approximation and parameter inference of non-Markovian models of gene expression.
Qingchao Jiang,Xiaoming Fu,Xiaoming Fu,Shifu Yan,Runlai Li,Wenli Du,Zhixing Cao,Feng Qian,Ramon Grima +8 more
TL;DR: In this article, an artificial neural network is used to approximate the time-dependent distributions of non-Markovian models by the solutions of simpler time-inhomogeneous Markovians, and the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters.
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A Systematic Min–Max Optimization Design of Constrained Model Predictive Tracking Control for Industrial Processes against Uncertainty
TL;DR: A systematic min-max optimization design of model predictive tracking control (MPC) for industrial processes under partial actuator uncertainty and unknown disturbances is proposed, where two-step optimization is adopted to further enhance the system performance.