CAJ | 학술논문

模糊产生式规则置信度的确定在很大程度上依赖专家的经验,难以获得精确的结果.针对该问题,将人工鱼群算法引入模糊Petri网(FPN)的置信度寻优过程中,提出一种基于改进人工鱼群算法的参数优化算法,不依赖于经验数据,对初始输入无严格要求.实验结果表明,该算法训练出的模糊Petri网参数正确率较高,能提高FPN的自学习能力,降低实际应用难度.
모호산생식규칙치신도적학정재흔대정도상의뢰전가적경험,난이획득정학적결과.침대해문제,장인공어군산법인입모호Petri망(FPN)적치신도심우과정중,제출일충기우개진인공어군산법적삼수우화산법,불의뢰우경험수거,대초시수입무엄격요구.실험결과표명,해산법훈련출적모호Petri망삼수정학솔교고,능제고FPN적자학습능력,강저실제응용난도.
Certainty Factor(CF) of fuzzy production rules depends on the experience of experts at a large extent,it is difficult to obtain accurate results.Aiming at this problem,Artificial Fish School Algorithm (AFSA) is introduced into the procedure of exploring the certainty factor parameters of Fuzzy Petri Net(FPN) and an parameters optimization algorithm based on improved AFSA is proposed.It does not depend on experiential data and the requirements for primary input are not critical.Experimental results show that the trained parameters gained from the algorithm are highly accurate and the strong self-learning capability of resultant FPN model can be improved,it reduces the difficulty of the practical application.