一种基于云模型的定性建模与仿真方法

C. Shao, Kelu Sun, Zhenzhong Shao
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引用次数: 0

摘要

定性仿真是处理不完全知识的有效方法,如何表征定性知识,减少虚假行为是其研究重点。针对现有方法表征定性知识和过滤虚假行为的局限性,提出了一种基于常规云模型的定性建模和仿真方法。本研究将云的数值特性和云的测量引入该方法;从而更准确地评估定性知识,拓展变量表达和数量空间;同时,通过更充分地刻画系统状态、更直观地转换状态、更准确地过滤虚假行为,构建了建模和仿真的手段。此外,在仿真算法中,采用云测量指标对现有约束手段无法确定的后继状态进行有效选择。仿真结果表明,该方法有效地提高了对虚假行为的过滤能力,是一种可行的方法。
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A qualitative modeling and simulation method based on cloud model
Qualitative simulation is effective methods processing incomplete knowledge, how to represent qualitative knowledge and reduce spurious behaviors is its research emphasis. Contraposing the limitations of existing methods representing qualitative knowledge and filtering spurious behaviors, an approach is proposed to qualitatively model and simulate on the basis of normal cloud model. In this research, the cloud numeric characteristics and cloud measurement are introduced into this approach; consequently, it can more accurately assess the qualitative knowledge, and expand the variables expression and quantity space; meanwhile, it is constructed the means of modeling and simulation by adopting more amply portraying the system status, more intuitively transforming status and more accurately filtering spurious behaviors. Furthermore, in simulation algorithm, cloud measurement indices are employed to significantly select the successor status which can not be determined by existing constraint means. The results of simulation example demonstrate that our approach effectively improves the ability to filter spurious behaviors, and it is a feasible method.
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