**4. Construction of the learning fuzzy Petri net model**

Petri net (PN) has ability to represent and analyze concurrency and synchronization phenomena in an easy way. PN approach can also be easily combined with other techniques and theories such as object-oriented programming, fuzzy theory, neural networks, etc. These modified PNs are widely used in the fields of manufacturing, robotics, knowledge based systems, process control, as well as other kinds of engineering applications [15]. Fuzzy Petri net (FPN), which combines PN and fuzzy theory, has been used for knowledge representation and reasoning in the presence of inexact data and knowledge based systems. But traditional FPN lacks of learning mechanism, it is the main weakness while modeling uncertain knowledge systems [25]. In this section, we propose a new learning model tool learning fuzzy Petri net (LFPN) [7]. Contrasting with the existing FPN, there are three extensions in the new model: 1) the place can possess different tokens which represent different propositions; 2) these propositions have different degrees of truth toward different transitions; 3) the truth degree of proposition can be learned through the arc's weight function adjusting. The LFPN model obtains the capability of fuzzy production rules learning through truth degree updating. The artificial neural network is gotten learning ability through weight adjusting. The LFPN learning algorithm which introduces network learning method into Petri net update is proposed and the convergence of algorithm is analyzed.
