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发布人:刘玉菡  发布时间:2020-11-18   浏览次数:231


时间:20201119日(周四) 晚上730-830

会议室:腾讯会议室ID945 885 532


Observability of Large-scale Boolean Control Networks with an Application to the T-cell Receptor Kinetics


Observability is a fundamental property in control theory. Verifying observability of Boolean control networks (BCNs) is NP-hard in the number of nodes. A BCN is observable if one can use an input sequence and the corresponding output sequence to determine the initial state. In this talk, we will review results on the observability verification problem of Boolean control networks, and will also introduce our recent results on whether and when a node-aggregation approach can be used to overcome the computational complexity in verifying observability. As an application, we use a BCN T-cell receptor kinetics model from the literature with 37 state nodes (i.e., 2^37 states) and 3 input nodes (2^3 inputs) to illustrate the efficiency of the results. For this model, we derive the unique minimal set of 16 state nodes needed to be directly measured to make the overall BCN observable.


Kuize Zhang received the B.S. and Ph.D. degrees in Mathematics and Control Science and Engineering from Harbin Engineering University, China, in 2009 and 2014, respectively. He is now a Humboldt Fellow at Technical University of Berlin, Germany. He held long-term visiting or research positions at KTH Royal Institute of Technology, Sweden (2017-20), Technical University of Munich, Germany (2016-17); Academy of Mathematics and Systems Science (2015-16), the Chinese Academy of Sciences; Nanyang Technological University, Singapore (2013-14); and University of Turku, Finland (2012-13). His current research interests include fundamental topics in discrete-event systems (finite automata and Petri nets, rewrote the fundamental results of detectability of finite automata and wrote the first detectability paper of labeled Petri nets), Boolean networks (solved the observability verification problem and proposed the notion of invertibility) with applications to systems biology, etc.