Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incom...Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incomprehensible behavioral descriptions and the simulation for complex systems frequently is intractable. Two model de- composition methods are proposed in this paper to eliminate or decrease the insujficiency of this algorithm. Using a directed graph to represent the qualitative model, the strongly connected graph based theory and genetic algorithm based model decomposition are proposed to decompose the model. A new simple system model is reconstructed by subgraphs and causal relations when the system directed graph is decomposed completely. Each sub-graph is viewed as a separate system and will be simulated separately, and the simulation result of causally upstream subsystem is used to constrain the behavior of downstream subsystems. The model decomposition algorithm provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used.展开更多
为提升开关电源设计效率和性能,针对普通设计者难以全面考虑各因素对电源性能影响的问题,提出基于因果推断与知识图谱的开关电源设计辅助方法.首先,以电感磁芯材料对电源性能影响为例,通过仿真与实物实验收集数据.然后,引入线性回归方...为提升开关电源设计效率和性能,针对普通设计者难以全面考虑各因素对电源性能影响的问题,提出基于因果推断与知识图谱的开关电源设计辅助方法.首先,以电感磁芯材料对电源性能影响为例,通过仿真与实物实验收集数据.然后,引入线性回归方法分析电感磁芯材料对电源性能的影响关系,并采用PC(Peter and Clack)算法挖掘电感磁芯材料与电源性能间还未明确的因果关系.接着,采用结构方程模型计算电感磁芯材料对电源性能的因果效应.引入知识图谱技术,构建含因果关系的电源知识图谱,为电源优化设计提供新视角并提升智能化水平.最后,通过案例分析验证了所提方法在电源设计中的有效性.展开更多
文摘Qualitative reasoning uses incomplete knowledge to compute a description of the possible behaviors for dynamic systems. A standard qualitative simulation（QSIM） algorithm frequently results in a large number of incomprehensible behavioral descriptions and the simulation for complex systems frequently is intractable. Two model de- composition methods are proposed in this paper to eliminate or decrease the insujficiency of this algorithm. Using a directed graph to represent the qualitative model, the strongly connected graph based theory and genetic algorithm based model decomposition are proposed to decompose the model. A new simple system model is reconstructed by subgraphs and causal relations when the system directed graph is decomposed completely. Each sub-graph is viewed as a separate system and will be simulated separately, and the simulation result of causally upstream subsystem is used to constrain the behavior of downstream subsystems. The model decomposition algorithm provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used.
文摘为提升开关电源设计效率和性能,针对普通设计者难以全面考虑各因素对电源性能影响的问题,提出基于因果推断与知识图谱的开关电源设计辅助方法.首先,以电感磁芯材料对电源性能影响为例,通过仿真与实物实验收集数据.然后,引入线性回归方法分析电感磁芯材料对电源性能的影响关系,并采用PC(Peter and Clack)算法挖掘电感磁芯材料与电源性能间还未明确的因果关系.接着,采用结构方程模型计算电感磁芯材料对电源性能的因果效应.引入知识图谱技术,构建含因果关系的电源知识图谱,为电源优化设计提供新视角并提升智能化水平.最后,通过案例分析验证了所提方法在电源设计中的有效性.
