摘要
本文针对模糊推理中常存在推理结果意义不明确的问题,提出应用带标识的模糊Petri网(MFPNs)进行模糊推理。推理的过程中考虑模糊产生式规则的权值、阈值、确定性因子等几种知识表示参数以获得更多信息。给出基于相似性测度的模糊推理算法,通过计算带标识的模糊Petri网的最终输出库所中的托肯值可以得到最终的模糊推理结果。通过实例可以验证这样得到的推理结果意义更明确,计算过程更加高效。
Aiming at the problem that the use of fuzzy Petri nets to make fuzzy reasoning is usually unclear in fuzzy reasoning, a marked fuzzy Petri net（MFPN） is presented to carry out fuzzy reasoning. Several knowledge representation parameters： weight, certainty factor and threshold value, etc, are considered during fuzzy reasoning to obtain more information. A reasoning algorithm based on similarity measures is proposed. The deduced consequence can be calculated according to computing the token value in the final output place. Through the verification of production examples, the meaning of the deduced consequence is clearer and the calculation process is more efficient.
出处
《计算机工程与科学》
CSCD
北大核心
2012年第3期152-157,共6页
Computer Engineering & Science
基金
安徽省高等学校省级自然科学研究项目(KJ2009B148Z
KJ2012Z322)
安徽省高等学校优秀青年人才基金项目(2009SQRZ157
2009SQRZ156)
