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Learning Petri Network with Functions Distribution

机译：通过功能分布学习Petri网络

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Large-scale complicated systems are required to be controlled timely and appropriately. A human brain has similar functions to those of a controller of the large-scale complicated systems; it scans and recognizes sensory inputs and outputs responses to the environments. Why does a human brain work skillfully? The key is the capability of functions distribution and learning. Functions distribution means that a specific part exists in the brain, in order to realize a specific function. For example, a live neural network has different acting parts corresponding to different network inputs or stimuli. In this paper, we have proposed a new brain-like model that we call Learning Petri Network (L. P. N.). The fundamental idea is to revise Petri Net. Petri Net is composed of state and transition and can control firing by tokens, so it is possibile for this net to realize functions distribution. The revising point is to give Petri Net the ability of learning as Neural Network (N. N.). And, it is the fundamental difference from N. N., that learning of the proposed method is carried out on the only network pass of the token transfer.
机译：大型复杂系统需要及时适当地控制。人脑具有与大型复杂系统的控制器相似的功能；它扫描并识别感官输入并输出对环境的响应。人脑为什么会熟练地工作？关键是功能分配和学习的能力。功能分布是指大脑中存在特定部位，以实现特定功能。例如，实时神经网络具有对应于不同网络输入或刺激的不同作用部分。在本文中，我们提出了一种新的类似于大脑的模型，称为学习陪替氏网络（L. P. N.）。基本思想是修改Petri Net。 Petri网由状态和过渡组成，并且可以通过令牌控制着火，因此该网有可能实现功能分配。修订要点是赋予Petri Net作为神经网络（N. N.）的学习能力。并且，与N. N.的根本区别是，在令牌传输的唯一网络通道上进行了所提出方法的学习。

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