Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
This paper studies the alarm response of false data injection attack in cyber-physical system under limited bandwidth constraints. Firstly, the false data injected in the actuator is modeled as unknown input, and the local residual generators are designed by the given H_infinity performance index to generate the residual signals approaching the attack signal. Subsequently, in order to improve the alarm response, all the residual signals are quantized and then send to the detection center under the distributed fusion framework, the optimization objective is designed where the H_infinity perf
Repairing the damaged road network, which mainly focuses on how to reasonably schedule the repair crew to quickly unblock the road network and ensure that rescue teams and emergency resources in the source node can be delivered to different demand nodes in time, is a basic premise for emergency disposal and rescue after the occurrence of extraordinary serious natural disasters. However, it is difficult for the existing methods to find a feasible scheduling strategy under enormous demand nodes. network model and the Markov decision-making process, based on which a double-feedback reward func
The existing multi-view graph learning methods are mainly based on the premise that the data has good completeness, and do not fully consider the learning problem on incomplete data caused by element missing. To address this issue, this paper proposes a multi-view graph learning method with incomplete data. On the one hand, the method puts the data reconstruction and graph learning into the unified framework within view, which learns the view specific neighbor relationship among samples from the reconstructed data to compensate for the influence of data missing on data distribution. On the
Due to the powerful feature representation capability of deep learning and the effective policy learning capability of reinforcement learning (RL), deep reinforcement learning (DRL) has made remarkable achievements in a series of complex sequential decision-making problems. With the popularity of DRL in many single-agent tasks, its application in multi-agent systems is flourishing. Recently, multi-agent deep reinforcement learning (MADRL) has attracted increasing attention in the field of artificial intelligence, and the scalability and transferability have become one of the important issue
The subway station air conditioning system consumes a lot of energy, and traditional control methods cannot take into account the comfort and energy saving issues together, resulting in poor control effect. Moreover, the current subway station air conditioning system and the control air systems the control water system separately, which cannot guarantee the energy saving effect of the whole system. Therefore, this paper proposes an energy-saving control strategy for the system based on reinforcement learning. Firstly, this paper uses a neural network to establish an air system modification