Author
Hongsheng Qi
Bio: Hongsheng Qi is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Boolean network & Boolean circuit. The author has an hindex of 22, co-authored 91 publications receiving 4802 citations.
Papers published on a yearly basis
Papers
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Book•
08 Apr 2011TL;DR: A new matrix product, called semi-tensor product of matrices, is used, which can covert the Boolean networks into discrete-time linear dynamic systems and the controllability of Boolean control networks is considered in the paper as an application.
Abstract: A Boolean network is a logical dynamic system, which has been used to describe cellular networks. Using a new matrix product, called semi-tensor product of matrices, a logical function can be expressed as an algebraic function. This expression can covert the Boolean networks into discrete-time linear dynamic systems. Similarly, the Boolean control networks can also be converted into discrete time bilinear dynamic systems. Under these forms the standard matrix analysis can be used to consider the structure and the control problems of Boolean (control) networks. After the detailed description of this new approach, the controllability of Boolean control networks is considered in the paper as an application.
834 citations
TL;DR: The controllability and observability of Boolean control networks are investigated and the controllable via two kinds of inputs is revealed by providing the corresponding reachable sets precisely.
702 citations
TL;DR: Under this framework, a Boolean network equation is converted into an equivalent algebraic form as a conventional discrete-time linear system, and a matrix expression of logic is proposed, where a logical variable is expressed as a vector, a logical function is express as a multiple linear mapping.
Abstract: A new matrix product, called semi-tensor product of matrices, is reviewed Using it, a matrix expression of logic is proposed, where a logical variable is expressed as a vector, a logical function is expressed as a multiple linear mapping Under this framework, a Boolean network equation is converted into an equivalent algebraic form as a conventional discrete-time linear system Analyzing the transition matrix of the linear system, formulas are obtained to show a) the number of fixed points; b) the numbers of cycles of different lengths; c) transient period, for all points to enter the set of attractors; and d) basin of each attractor The corresponding algorithms are developed and used to some examples
589 citations
TL;DR: In this paper, the stability of Boolean networks and the stabilization of Boolean control networks are investigated using semi-tensor product of matrices and the matrix expression of logic, which can be converted to a discrete time linear (bilinear) dynamics, called the algebraic form of the Boolean (control) network.
Abstract: The stability of Boolean networks and the stabilization of Boolean control networks are investigated. Using semi-tensor product of matrices and the matrix expression of logic, the dynamics of a Boolean (control) network can be converted to a discrete time linear (bilinear) dynamics, called the algebraic form of the Boolean (control) network. Then the stability can be revealed by analyzing the transition matrix of the corresponding discrete time system. Main results consist of two parts: (i) Using logic coordinate transformation, the known sufficient condition based on incidence matrix has been improved. It can also be used in stabilizer design. (ii) Based on algebraic form, necessary and sufficient conditions for stability and stabilization, respectively, are obtained. Copyright © 2010 John Wiley & Sons, Ltd.
353 citations
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Journal Article•
28,685 citations
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations