The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Chunhua Yang - One of the best experts on this subject based on the ideXlab platform.
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stability and set stability in distribution of probabilistic boolean networks
IEEE Transactions on Automatic Control, 2019Co-Authors: Rongpei Zhou, Yuhu Wu, Chunhua YangAbstract:We propose a new concept, stability in distribution (SD) of a probabilistic Boolean network (PBN), which determines whether the probability distribution converges to the distribution of the target state (namely, a one-point distributed random variable). In a PBN, stability with probability one, stability in the stochastic sense, and SD are equivalent. The SD is easily generalized to Subset stability, i.e., to set stability in distribution (SSD). We prove that the transition probability from any state to an Invariant Subset (or to a fixed point) is nondecreasing in time. This monotonicity is an important property in establishing stability criteria and in calculating or estimating the transient period. We also obtain a verifiable, necessary, and sufficient condition for SD of PBNs with independently and identically distributed switching. We then show that SD problems of PBNs with Markovian switching and PBN synchronizations can be recast as SSD problems of Markov chains. After calculating the largest Invariant Subset of a Markov chain in a given set by the newly proposed algorithm, we propose a necessary and sufficient condition for SSDs of Markov chains. The proposed method and results are supported by examples.
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set stability and set stabilization of boolean control networks based on Invariant Subsets
Automatica, 2015Co-Authors: Pan Wang, Chunhua YangAbstract:This study addresses the set stability of Boolean networks (BNs) and set stabilization of Boolean control networks (BCNs). Set stability determines whether a BN converges to a given Subset, whereas set stabilizability addresses the issue of whether a BCN can be stabilized to a given Subset. Many problems can be viewed as special cases of set stability and set stabilization, including synchronization, partial stability, and partial stabilization problems. The concepts of Invariant Subset and control Invariant Subset are introduced. Then, algorithms for the largest Invariant Subset and the largest control Invariant Subset contained in a given Subset are proposed. Based on the Invariant Subsets obtained, the necessary and sufficient conditions for set stability and set stabilizability are established, and formulae are provided to calculate the shortest transient periods for respective initial states. A design procedure is proposed for finding all the time-optimal set stabilizers. Finally, an example is used to show the application of the proposed results to the synchronization problem of BNs.
B S Manjunath - One of the best experts on this subject based on the ideXlab platform.
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rotation Invariant texture classification using a complete space frequency model
IEEE Transactions on Image Processing, 1999Co-Authors: G M Haley, B S ManjunathAbstract:A method of rotation-Invariant texture classification based on a complete space-frequency model is introduced. A polar, analytic form of a two-dimensional (2-D) Gabor wavelet is developed, and a multiresolution family of these wavelets is used to compute information-conserving microfeatures. From these microfeatures a micromodel, which characterizes spatially localized amplitude, frequency, and directional behavior of the texture, is formed. The essential characteristics of a texture sample, its macrofeatures, are derived from the estimated selected parameters of the micromodel. Classification of texture samples is based on the macromodel derived from a rotation Invariant Subset of macrofeatures. In experiments, comparatively high correct classification rates were obtained using large sample sets.
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rotation Invariant texture classification using modified gabor filters
International Conference on Image Processing, 1995Co-Authors: G M Haley, B S ManjunathAbstract:A method of rotation Invariant texture classification based on a joint space-frequency model is introduced. Multiresolution filters, based on a truly analytic form of a polar 2-D Gabor (1946) wavelet, are used to compute spatial frequency-specific but spatially localized microfeatures. These microfeatures constitute an approximate basis set for the representation of the texture sample. The essential characteristics of a texture sample, its macrofeatures, are derived from the statistics of its microfeatures. A texture is modeled as a multivariate Gaussian distribution of macrofeatures. Classification is based on a rotation Invariant Subset of macrofeatures.
Rongpei Zhou - One of the best experts on this subject based on the ideXlab platform.
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Asymptotical Feedback Set Stabilization of Probabilistic Boolean Control Networks
IEEE transactions on neural networks and learning systems, 2020Co-Authors: Rongpei Zhou, Yuqian Guo, Weihua GuiAbstract:In this article, we investigate the asymptotical feedback set stabilization in distribution of probabilistic Boolean control networks (PBCNs). We prove that a PBCN is asymptotically feedback stabilizable to a given Subset if and only if (iff) it constitutes asymptotically feedback stabilizable to the largest control-Invariant Subset (LCIS) contained in this Subset. We proposed an algorithm to calculate the LCIS contained in any given Subset with the necessary and sufficient condition for asymptotical set stabilizability in terms of obtaining the reachability matrix. In addition, we propose a method to design stabilizing feedback based on a state-space partition. Finally, the results were applied to solve asymptotical feedback output tracking and asymptotical feedback synchronization of PBCNs. Examples were detailed to demonstrate the feasibility of the proposed method and results.
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stability and set stability in distribution of probabilistic boolean networks
IEEE Transactions on Automatic Control, 2019Co-Authors: Rongpei Zhou, Yuhu Wu, Chunhua YangAbstract:We propose a new concept, stability in distribution (SD) of a probabilistic Boolean network (PBN), which determines whether the probability distribution converges to the distribution of the target state (namely, a one-point distributed random variable). In a PBN, stability with probability one, stability in the stochastic sense, and SD are equivalent. The SD is easily generalized to Subset stability, i.e., to set stability in distribution (SSD). We prove that the transition probability from any state to an Invariant Subset (or to a fixed point) is nondecreasing in time. This monotonicity is an important property in establishing stability criteria and in calculating or estimating the transient period. We also obtain a verifiable, necessary, and sufficient condition for SD of PBNs with independently and identically distributed switching. We then show that SD problems of PBNs with Markovian switching and PBN synchronizations can be recast as SSD problems of Markov chains. After calculating the largest Invariant Subset of a Markov chain in a given set by the newly proposed algorithm, we propose a necessary and sufficient condition for SSDs of Markov chains. The proposed method and results are supported by examples.
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Set Stabilization in Distribution of Probabilistic Boolean Control Networks
2018 13th World Congress on Intelligent Control and Automation (WCICA), 2018Co-Authors: Rongpei Zhou, Yuqian GuoAbstract:In this paper, we propose the concept of set stabilization in distribution of a probabilistic Boolean control network (PBCN), which determines whether the probability distribution converges to the distribution of the destination state Subset under a certain control sequence. Then an algorithm is proposed to calculate the largest control Invariant Subset (LCIS) of a PBCN contained in a given set. We prove that for a PBCN, set stabilizability in distribution and set stabilizability in distribution by state feedback are equivalent. Based on this, a necessary and sufficient condition for set stabilizability in distribution is derived, and a control design method is proposed. Finally, examples are provided to illustrate the effectiveness of the proposed method.
Pan Wang - One of the best experts on this subject based on the ideXlab platform.
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set stability and set stabilization of boolean control networks based on Invariant Subsets
Automatica, 2015Co-Authors: Pan Wang, Chunhua YangAbstract:This study addresses the set stability of Boolean networks (BNs) and set stabilization of Boolean control networks (BCNs). Set stability determines whether a BN converges to a given Subset, whereas set stabilizability addresses the issue of whether a BCN can be stabilized to a given Subset. Many problems can be viewed as special cases of set stability and set stabilization, including synchronization, partial stability, and partial stabilization problems. The concepts of Invariant Subset and control Invariant Subset are introduced. Then, algorithms for the largest Invariant Subset and the largest control Invariant Subset contained in a given Subset are proposed. Based on the Invariant Subsets obtained, the necessary and sufficient conditions for set stability and set stabilizability are established, and formulae are provided to calculate the shortest transient periods for respective initial states. A design procedure is proposed for finding all the time-optimal set stabilizers. Finally, an example is used to show the application of the proposed results to the synchronization problem of BNs.
Thompson, Daniel J. - One of the best experts on this subject based on the ideXlab platform.
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Measures of maximal entropy on subsystems of topological suspension semi-flows
2021Co-Authors: Kucherenko Tamara, Thompson, Daniel J.Abstract:Given a compact topological dynamical system (X, f) with positive entropy and upper semi-continuous entropy map, and any closed Invariant Subset $Y \Subset X$ with positive entropy, we show that there exists a continuous roof function such that the set of measures of maximal entropy for the suspension semi-flow over (X,f) consists precisely of the lifts of measures which maximize entropy on Y. This result has a number of implications for the possible size of the set of measures of maximal entropy for topological suspension flows. In particular, for a suspension flow on the full shift on a finite alphabet, the set of ergodic measures of maximal entropy may be countable, uncountable, or have any finite cardinality.Comment: v3: 10 pages. Corrected some typos. To appear in Studia Mathematic
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Measures of maximal entropy on subsystems of topological suspension semi-flows
2019Co-Authors: Kucherenko Tamara, Thompson, Daniel J.Abstract:Given a compact topological dynamical system (X, f) with positive entropy and upper semi-continuous entropy map, and any closed Invariant Subset $Y \Subset X$ with positive entropy, we show that there exists a continuous roof function such that the set of measures of maximal entropy for the suspension semi-flow over (X,f) consists precisely of the lifts of measures which maximize entropy on Y. This result has a number of implications for the possible size of the set of measures of maximal entropy for topological suspension flows. In particular, for a suspension flow on the full shift on a finite alphabet, the set of ergodic measures of maximal entropy may be countable, uncountable, or have any finite cardinality.Comment: v2: 10 pages. Corrected a typo and explained better the punchline of the proof of the main theorem (end of p.7
