Reasoning dynamic fuzzy systems based on adaptive fuzzy higher order Petri nets
Reasoning dynamic fuzzy systems based on adaptive fuzzy higher order Petri nets
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DOI：
10.1016/j.ins.2014.07.011
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发表时间：
2014-12
期刊：
影响因子：
--
通讯作者：
Mohamed Amin;Doaa Shebl
中科院分区：
文献类型：
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作者：
Mohamed Amin;Doaa Shebl
Petri网和模糊Petri网作为建模的形式化方法，对弧权值的变化适应性较差。权值是表示由（模糊）Petri网建模的系统的新传入数据的参数。权值的变化满足各种应用领域（如智能E-learning、计算机数控、天气预报和专家系统）中的系统变化。在本文中，我们介绍了一类新的模糊Petri网，考虑到在模糊推理过程中的弧的重量变化。给出了模型的形式化描述、权值学习算法、模糊推理算法、新状态方程的代数形式以及稳定性分析。最后，我们介绍了一个天气预报应用程序作为一个真实的例子。
Petri nets and Fuzzy Petri nets as modeling formalism are not adaptable according to the changes of the arc weight. Weights are the parameters that represent the new incoming data of the system modeled by a (Fuzzy) Petri net. The weight changes meet the system changes in a variety of application domains (e.g. Intelligent E-learning, Computer Numerical Control, Weather Forecasting, and Expert Systems). In this paper, we introduce a new class of Fuzzy Petri nets that takes into account the weight changes of the arc in the Fuzzy reasoning process. We give the formal description of the model, an algorithm for learning the weights without the need to transfer into a neural network, an algorithm for the fuzzy reasoning, the algebraic forms of the new state equation, and the stability analysis. Finally, we introduce a weather forecasting application as a real example.