Dynamic properties of fuzzy Petri net model and related analysis

Fuzzy Petri net(FPN) has been extensively applied in industrial fields for knowledge-based systems or systems with uncertainty.Although the applications of FPN are known to be successful,the theoretical research of FPN is still at an initial stage.To pave a way for further study,this work explores related dynamic properties of FPN including reachability,boundedness,safeness,liveness and fairness.The whole methodology is divided into two phases.In the first phase,a comparison between elementary net system(EN_system) and FPN is established to prove that the FPN is an extensive formalism of Petri nets using a backwards-compatible extension method.Next,current research results of dynamic properties are utilized to analyze FPN model.The results illustrate that FPN model is bounded,safe,weak live and fair,and can support theoretical evidences for designing related decomposition algorithm.

Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation Algorithm for Information Services

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.

Designing Genetic Regulatory Networks Using Fuzzy Petri Nets Approach

In this paper, we have successfully presented a fuzzy Petri net （FPN） model to design the genetic regulatory network. Based on the FPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. By using the reasoning algorithm for the FPN, we present an alternative approach that is more promising than the fuzzy logic. The proposed FPN approach offers more flexible reasoning capability because it is able to obtain results with fuzzy intervals rather than point values. In this paper, a novel model with a new concept of hidden fuzzy transition （HFT） to design the genetic regulatory network is developed. We have built the FPN model and classified the input data in terms of time point and obtained the output data, so the system can be viewed as the two-input and one output system. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. The experimental results show the proposed approach is feasible and acceptable to design the genetic regulatory network and investigate the dynamical behaviors of gene network.

Method of Dynamic Knowledge Representation and Learning Based on Fuzzy Petri Nets

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.

NNF and NNPrF—Fuzzy Petri Nets Based on Neural Network for Knowledge Representation, Reasoning and Learning

This paper proposes NNF - a fuzzy Petri Net system based on neural network for proposition logic representation, and gives the formal definition of NNF. For the NNF model, forward reasoning algorithm, backward reason-ing algorithm and knowledge learning algorithm are discussed based on weight training algorithm of neural network - Back Propagation algorithm. Thus NNF is endowed with the ability of learning a rule. The paper concludes with a discussion on extending NNF to predicate logic, forming NNPrF, and proposing the formal definition and a reasoning algorithm of NNPrF.

FINE-GRAINED DISTRIBUTED MULTIMEDIA SYNCHRONIZA-TION MODEL——ENHANCED FUZZY-TIMING PETRI NET

A fine grained distributed multimedia synchronization model——Enhanced Fuzzy timing Petri Net was proposed which is good at modeling indeterminacy and fuzzy. To satisfy the need of maximum tolerable jitter, the sufficient conditions are given in intra object synchronization. Method to find a proper granularity in inter object synchronization is also given to satisfy skew. Exceptions are detected and corrected as early as possible using restricted blocking method.