The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller （S-C） and contraller-to-actuator （C-A） random network-induced ...The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller （S-C） and contraller-to-actuator （C-A） random network-induced delays are modeled as two Markov chains. The focus is on the design of a two-mode-dependent guar- anteed cost controller, which depends on both the current S-C delay and the most recently available C-A delay. The resulting closed-loop systems are special jump linear systems. Sufficient conditions for existence of guaranteed cost controller and an upper bound of cost function are established based on stochastic Lyapunov-Krasovakii functions and linear matrix inequality （LMI） approach. A simulation example illustrates the effectiveness of the proposed method.展开更多
Markov network ts an another powerful tool besides Bayesian network which can be used to do uncertain inference. A method of learning Markov network automaticly from mass data based on boundary has been discussed in t...Markov network ts an another powerful tool besides Bayesian network which can be used to do uncertain inference. A method of learning Markov network automaticly from mass data based on boundary has been discussed in this paper. Taking advantage of an important conclusion in information theory ,we present an efficient boundary based Markov network learning algorithm. This algorithm only demands O(N2) times CI (conditional independence) test. We prove if the joint probability is strictly positive,then the found Markov network must be the minimal I_map of the sample.展开更多
基金supported by the NSFC-Guangdong Joint Foundation Key Project(U0735003)the Overseas Cooperation Foundation(60828006)+1 种基金the Scientific Research Foundation for Returned Overseas Chinese Scholars,State Education Ministry,the Fundamental Research Funds for the Central Universities(2009ZM0076)the Natural Science Foundation of Guangdong Province(06105413)
文摘The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller （S-C） and contraller-to-actuator （C-A） random network-induced delays are modeled as two Markov chains. The focus is on the design of a two-mode-dependent guar- anteed cost controller, which depends on both the current S-C delay and the most recently available C-A delay. The resulting closed-loop systems are special jump linear systems. Sufficient conditions for existence of guaranteed cost controller and an upper bound of cost function are established based on stochastic Lyapunov-Krasovakii functions and linear matrix inequality （LMI） approach. A simulation example illustrates the effectiveness of the proposed method.
文摘Markov network ts an another powerful tool besides Bayesian network which can be used to do uncertain inference. A method of learning Markov network automaticly from mass data based on boundary has been discussed in this paper. Taking advantage of an important conclusion in information theory ,we present an efficient boundary based Markov network learning algorithm. This algorithm only demands O(N2) times CI (conditional independence) test. We prove if the joint probability is strictly positive,then the found Markov network must be the minimal I_map of the sample.
