Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production rules.In order to solve the problem of poor self-learnin...Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production rules.In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems,a new Petri net modeling method is proposed by introducing BP(Error Back Propagation)algorithm in neural networks.By judging whether the transition is ignited by continuous function,the intuitionistic fuzziness of classical BP algorithm is extended to the parameter learning and training,which makes Petri network have stronger generalization ability and adaptive function,and the reasoning result is more accurate and credible,which is useful for information services.Finally,a typical example is given to verify the effectiveness and superiority of the parameter optimization method.展开更多
A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The...A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The advantages of knowledge representation based on production rules and neural networks were integrated into this method. Just as production knowledge representation, this method has clear structure and specific parameters meaning. In addition, it has learning and parallel reasoning ability as neural networks knowledge representation does. The result of simulation shows that the learning algorithm can converge, and the parameters of weights, threshold value and certainty factor can reach the ideal level after training.展开更多
This paper presented a fuzzy Petri net model to deal with the monitoring of robotic assembly. Based on the fuzzy Petri net model, an efficient composite reasoning mode was proposed to perform fuzzy reasoning automatie...This paper presented a fuzzy Petri net model to deal with the monitoring of robotic assembly. Based on the fuzzy Petri net model, an efficient composite reasoning mode was proposed to perform fuzzy reasoning automatiealy. It can determine whether there exists an antecedent-consequence relationship between two contact states. Furthermore, various types of sensor signals can be converted to the same form of real values between zero and one, and the contradiction among large number, high degree of truth and importance of input conditions can be resolved very well by introducing the weight factors and priorities for sensor signals. Finally, a peg- in-the-hole example was given to illustrate the reasonability and feasibility of the proposed model.展开更多
文摘Intuitionistic fuzzy Petri net is an important class of Petri nets,which can be used to model the knowledge base system based on intuitionistic fuzzy production rules.In order to solve the problem of poor self-learning ability of intuitionistic fuzzy systems,a new Petri net modeling method is proposed by introducing BP(Error Back Propagation)algorithm in neural networks.By judging whether the transition is ignited by continuous function,the intuitionistic fuzziness of classical BP algorithm is extended to the parameter learning and training,which makes Petri network have stronger generalization ability and adaptive function,and the reasoning result is more accurate and credible,which is useful for information services.Finally,a typical example is given to verify the effectiveness and superiority of the parameter optimization method.
文摘A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The advantages of knowledge representation based on production rules and neural networks were integrated into this method. Just as production knowledge representation, this method has clear structure and specific parameters meaning. In addition, it has learning and parallel reasoning ability as neural networks knowledge representation does. The result of simulation shows that the learning algorithm can converge, and the parameters of weights, threshold value and certainty factor can reach the ideal level after training.
基金Sponsored by the National High Technology Research and Development Prgram of China(Grant No2001AA42250)
文摘This paper presented a fuzzy Petri net model to deal with the monitoring of robotic assembly. Based on the fuzzy Petri net model, an efficient composite reasoning mode was proposed to perform fuzzy reasoning automatiealy. It can determine whether there exists an antecedent-consequence relationship between two contact states. Furthermore, various types of sensor signals can be converted to the same form of real values between zero and one, and the contradiction among large number, high degree of truth and importance of input conditions can be resolved very well by introducing the weight factors and priorities for sensor signals. Finally, a peg- in-the-hole example was given to illustrate the reasonability and feasibility of the proposed model.
