CAJ | 학술논문

针对复杂系统中不确定性信息的演变特性，提出对其动态适应的基于模糊 Petri 网和遗传-粒子群( GPSO)算法的不确定性知识表示方法。在基于模糊Petri网的不确定性知识表示模型的基础上，对该模型进行精确数学表示，并采用 GPSO实现对不确定性表征参数的动态求解和自学习。最后通过在运载火箭伺服机构故障诊断上的应用验证基于GPSO的自学习模糊Petri网的有效性。
침대복잡계통중불학정성신식적연변특성，제출대기동태괄응적기우모호 Petri 망화유전-입자군( GPSO)산법적불학정성지식표시방법。재기우모호Petri망적불학정성지식표시모형적기출상，대해모형진행정학수학표시，병채용 GPSO실현대불학정성표정삼수적동태구해화자학습。최후통과재운재화전사복궤구고장진단상적응용험증기우GPSO적자학습모호Petri망적유효성。
To represent and reason uncertain knowledge in the complex system dynamically and effectively, a self-adaptive method based on fuzzy-Petri net ( FPN ) and genetic particle swarm optimization ( GPSO ) algorithm is proposed. In this method, the knowledge-representation model based on FPN is established to build the mathematical model. And the GPSO is used in self-learning of the uncertain parameters to achieve self-adaptation of the model. Finally, a servo mechanism fault diagnose of launch vehicle is used to verify the proposed method.