摘要
针对模糊Petri网自学习能力差的缺点,在分析研究已有研究成果的基础上,指出了已有模糊Petri网分层算法的不足之处,并提出了一种具有普遍适用性的FPN分层算法。在该分层算法中,同一个库所的输出变迁被置于同一层次结构之中,在必要时可增加相应的虚库所和虚变迁,避免了同一层中变迁触发冲突问题的发生。该分层算法将神经网络理论引入模糊Petri网的研究中,实验验证了其正确性。
Aiming at the drawback of poor self-learning ability in fuzzy Petri net （FPN）, on the basis of analyzing and studying previous research results, the deficiency of existing delaminating algorithm about FPN is pointed out, and a FPN delaminating algorithm with general applicability is presented. In the delaminating algorithm, the output transition of same place is put in the same level, if necessary, the virtual place and transition can be added, which avoids the collision of the transition trigger in the same level. This delaminating algorithm introduces neural network theory into the FPN studying and its correctness is verified by experiment.
出处
《计算机时代》
2010年第3期27-29,共3页
Computer Era
基金
渭南师范学院研究生科研项目(09YKZ020)
