CAJ | 학술논문

信息物理融合系统( CPS)应用日趋广泛，如何使系统自主捕捉到复杂状态变化并做出相应动作是CPS的核心问题之一。针对此关键问题，结合强化学习算法提出一种基于相似度计算的CPS自决策方法( SCBRLA)。该方法首先提取系统特征和系统目标状态特征，然后通过计算系统当前状态与目标状态的相似度决定采取相应动作及其顺序。该方法可较好地用于分析CPS服务在受到攻击时，系统采取的自适应决策。仿真结果表明该方法能够帮助CPS系统实现自决策，且与传统算法相比能获得更快的响应速度。
신식물리융합계통( CPS)응용일추엄범，여하사계통자주포착도복잡상태변화병주출상응동작시CPS적핵심문제지일。침대차관건문제，결합강화학습산법제출일충기우상사도계산적CPS자결책방법( SCBRLA)。해방법수선제취계통특정화계통목표상태특정，연후통과계산계통당전상태여목표상태적상사도결정채취상응동작급기순서。해방법가교호지용우분석CPS복무재수도공격시，계통채취적자괄응결책。방진결과표명해방법능구방조CPS계통실현자결책，차여전통산법상비능획득경쾌적향응속도。
Cyber-Physical systems ( CPS ) are getting more and more popular. How to make the system automatically capture the changes of complex statuses and take proper actions responding to the changes is one of the key problems of CPS. Combining with the reinforcement learning algorithm, a novel self-decision algorithm of CPS based on similarity computation, called Similarity Computation Based on Reinforcement Learning Algorithm( SCBRLA) , is proposed to solve this problem. In this algorithm, the features of both system and system targets are firstly extracted, and then the similarities of current system state and target states are computed. Based on the computation result, system takes corresponding actions and decides the execution order of those actions. The proposed algorithm can be well used to analyze strategies of system self-decision when it receives attacks. The simulation results show that the proposed algorithm can help systems realize self-decision, and it has faster response speed compared with traditional method.