Fuzzy reasoning Petri nets

This paper presents a fuzzy reasoning Petri net (FRPN) model to represent a fuzzy production rule-based system. The issues of how to represent and reason about rules containing negative literals are addressed in the proposed PN model. The execution rules based on the model are defined formally using the operators in max-algebra. Then, a fuzzy reasoning algorithm is proposed to perform fuzzy reasoning automatically. The algorithm is consistent with the matrix equation expression method in the traditional PNs and allows one to exploit the maximum parallel reasoning potential embedded in the model. The legitimacy and feasibility of the proposed approach are proved and validated through a turbine fault diagnosis expert system.     

Fuzzy backward reasoning using fuzzy Petri nets

Chen, Ke and Chang (1990) have presented a fuzzy forward reasoning algorithm for rule-based systems using fuzzy Petri nets. In this paper, we extend the work of Chen, Ke and Chang (1990) to present a fuzzy backward reasoning algorithm for rule-based systems using fuzzy Petri nets, where the fuzzy production rules of a rule-based system are represented by fuzzy Petri nets. The system can perform fuzzy backward reasoning automatically to evaluate the degree of truth of any proposition specified by the user. The fuzzy backward reasoning capability allows the computers to perform reasoning in a more flexible manner and to think more like people.     

Modeling uncertainty reasoning with possibilistic Petri nets

Manipulation of perceptions is a remarkable human capability in a wide variety of physical and mental tasks under fuzzy or uncertain surroundings. Possibilistic reasoning can be treated as a mechanism that mimics human inference mechanisms with uncertain information. Petri nets are a graphical and mathematical modeling tool with powerful modeling and analytical ability. The focus of this paper is on the integration of Petri nets with possibilistic reasoning to reap the benefits of both formalisms. This integration leads to a possibilistic Petri nets model (PPN) with the following features. A possibilistic token carries information to describe an object and its corresponding possibility and necessity measures. Possibilistic transitions are classified into four types: inference transitions, duplication transitions, aggregation transitions, and aggregation-duplication transitions. A reasoning algorithm, based on possibilistic Petri nets, is also presented to improve the efficiency of possibilistic reasoning and an example related to diagnosis of cracks in reinforced concrete structures is used to illustrate the proposed approach.     

A reasoning algorithm for high-level fuzzy Petri nets

We introduce an automated procedure for extracting information from knowledge bases that contain fuzzy production rules. The knowledge bases considered here are modeled using the high-level fuzzy Petri nets proposed by the authors in the past. Extensions to the high-level fuzzy Petri net model are given to include the representation of partial sources of information. The case of rules with more than one variable in the consequent is also discussed. A reasoning algorithm based on the high-level fuzzy Petri net model is presented. The algorithm consists of the extraction of a subnet and an evaluation process. In the evaluation process, several fuzzy inference methods can be applied. The proposed algorithm is similar to another procedure suggested by Yager (1983), with advantages concerning the knowledge-base searching when gathering the relevant information to answer a particular kind of query     

Weighted fuzzy reasoning using weighted fuzzy Petri nets

This paper presents a Weighted Fuzzy Petri Net model (WFPN) and proposes a weighted fuzzy reasoning algorithm for rule-based systems based on Weighted Fuzzy Petri Nets. The fuzzy production rules in the knowledge base of a rule-based system are modeled by Weighted Fuzzy Petri Nets, where the truth values of the propositions appearing in the fuzzy production rules and the certainty factors of the rules are represented by fuzzy numbers. Furthermore, the weights of the propositions appearing in the rules are also represented by fuzzy numbers. The proposed weighted fuzzy reasoning algorithm can allow the rule-based systems to perform fuzzy reasoning in a more flexible and more intelligent manner     

Temporal knowledge representation and reasoning techniques usingtime Petri nets

In this paper, we present temporal knowledge representation and reasoning techniques using time Petri nets. A method is also proposed to check the consistency of the temporal knowledge. The proposed method can overcome the drawback of the one presented in Yao (1994). It provides a useful way to check the consistency of the temporal knowledge.     

基于直觉模糊Petri网的加权直觉模糊推理

利用直觉模糊集合较好地表现不确定信息的能力和Petri网的并行处理能力,构建了直觉模糊Petri网模型。给出了输入权值、变迁阈值等多种约束条件下的直觉模糊推理算法。该算法将直觉模糊推理过程转化为矩阵的运算过程可充分利用直觉模糊Petri网的并行推理能力,有效地避免同一变迁不必要地重复激发从而节省推理时间。实例分析表明所给出的直觉模糊推理算法较已有算法更加合理并且高效。     

Behavioral Petri nets: a model for diagnostic knowledgerepresentation and reasoning

Some of the most popular approaches to model-based diagnosis consist of reasoning about a model of the behaviour of the system to be diagnosed by considering a set of observations about such a system and by explaining it in terms of a set of initial causes. This process has been widely modeled via logical formalisms essentially taking into account declarative aspects. In this paper, a new approach is proposed, where the diagnostic process is captured within a framework based on the formalism of Petri nets. We introduce a particular net model, called Behavioral Petri Net (BPN), We show how the formalization of the diagnostic process can be obtained in terms of reachability in a BPN and can be implemented by exploiting classical analysis techniques of Petri nets like reachability graph analysis and P-invariant computation. Advantages of the proposed methods, like suitability to parallel processing and exploitation of linear algebra techniques, are then pointed out.     

Fuzzy rule base systems verification using high-level Petri nets

In this paper, we propose a Petri nets formalism for the verification of rule-based systems. Typical structural errors in a rule-based system are redundancy, inconsistency, incompleteness, and circularity. Since our verification is based on Petri nets and their incidence matrix, we need to transform rules into a Petri nets first, then derive an incidence matrix from the net. In order to let fuzzy rule-based systems detect above the structural errors, we are presenting a Petri-nets-based mechanism. This mechanism consists of three phases: rule normalization, rules transformation, and rule verification. Rules will be first normalized into Horn clauses, then transform the normalized rules into a high-level Petri net, and finally we verify these normalized rules. In addition, we are presenting our approach to simulate the truth conditions which still hold after a transition firing and negation in Petri nets for rule base modeling. In this paper, we refer to fuzzy rules as the rules with certainty factors, the degree of truth is computed in an algebraic form based on state equation which can be implemented in matrix computation in Petri nets. Therefore, the fuzzy reasoning problems can be transformed as the liner equation problems that can be solved in parallel. We have implemented a Petri nets tool to realize the mechanism presented fuzzy rules in this paper.     

Intelligent decision making in disassembly process based on fuzzy reasoning Petri nets

Practical disassembly process planning is extremely important for efficient material recycling and components reuse. The research work for the process planning in literature focuses on the generation of optimal sequences based on the predictive information of products. The used products, unfortunately, exhibit high uncertainty since products may experience very different conditions during their use stage. The indeterminate characteristics associated to used products often makes the predetermined plan unrealistic. Their disassembly process has to be decided dynamically adaptive to the products' specific status. To be able to deal with uncertainty in a dynamic decision making process, this paper presents a fuzzy reasoning Petri net (FRPN) model to represent related decision making rules in disassembly process. Using the proposed fuzzy reasoning algorithm based on the FRPN model, the multicriterion disassembly rules can be considered in the parallel way to make the decision automatically and quickly. Instead of producing the disassembly sequences before disassembling a whole product, the proposed method makes intelligent decisions based on dynamically updated status of components in the product at each disassembly step. Therefore, it is adaptive to the changes that arise during the process. Finally, an example is used to illustrate the application of the proposed methodology.     

Error-correcting Petri nets

The paper introduces error-correcting Petri nets, an algebraic methodology for designing synthetic biologic systems with monitoring capabilities. Linear error-correcting codes are used to extend the net’s structure in a way that allows for the algebraic detection and correction of non-reachable net markings. The presented methodology is based on modulo-p Hamming codes—which are optimal for the modulo-p correction of single errors—but also works with any other linear error-correcting code.     

Statechartable Petri nets

Petri nets and statecharts can model concurrent systems in a succinct way. While translations from statecharts to Petri nets exist, a well-defined translation from Petri nets to statecharts is lacking. Such a translation should map an input net to a corresponding statechart, having a structure and behaviour similar to that of the input net. Since statecharts can only model a restricted form of concurrency, not every Petri net has a corresponding statechart. We identify a class of Petri nets, called statechartable nets, that can be translated to corresponding statecharts. Statechartable Petri nets are structurally defined using the novel notion of an area. We also define a structural translation that maps each statechartable Petri net to a corresponding statechart. The translation is proven sound and complete for statechartable Petri nets.     

Recursive Petri nets

In order to design and analyse complex systems, modelers need formal models with two contradictory requirements: a high expressivity and the decidability of behavioural property checking. Here we present and develop the theory of such a model, the recursive Petri nets. First, we show that the mechanisms supported by recursive Petri nets enable to model patterns of discrete event systems related to the dynamic structure of processes. Furthermore, we prove that these patterns cannot be modelled by ordinary Petri nets. Then we study the decidability of some problems: reachability, finiteness and bisimulation. At last, we develop the concept of linear invariants for this kind of nets and we design efficient computations specifically tailored to take advantage of their structure.     

Possibilistic Petri nets

This paper presents the possibilistic Petri net model which combines possibility logic with Petri nets with objects. The main feature of this model is to allow one to reason about the aspects of uncertainty and change in dynamic discrete event systems. The paper presents relevant concepts of Petri nets with objects and possibility logic and how imprecision and vagueness are introduced in the marking of a Petri net with objects. The marking of a net is imprecise, or in a more general way, fuzzy, in order to represent an ill-known knowledge about a system state. A new marking updating according to the fuzzy marking such defined is also discussed. An example of shop door monitoring is presented that illustrates our approach.     

Normal Petri nets

A class of Petri nets, called normal Petri nets, is introduced, and it is shown that, for each initial marking, the reachability set of a normal marked Petri net is an effectively computable semilinear set. More generally, we show that the reachability set of a marked Petri net is an effectively computable semilinear set unless the total number of tokens in a minimal circuit is decreased to 0. We also show that a Petri net is normal if and only if it is weakly persistent for each initial marking without token-free circuits.     

模糊Petri网在压缩机故障诊断中的应用

为克服Petri网在推理分析复杂、不确定的故障信息中的不足,引入置信度最大及深度搜索优先的诊断方法,将Petri网和模糊推理知识相结合,提出模糊Petri网故障诊断方法及其概念与规则表示,采用反向推理算法根据已发生的故障来定位故障源,给出推理算法的具体步骤。通过逻辑推理和离心式压缩机故障的实例分析,验证了该算法的有效性和可行性,提高了故障诊断的准确性和高效性。     

A model of fuzzy spatio-temporal knowledge representation and reasoning based on high-level Petri nets

In many application areas there is a need to represent human-like knowledge related to spatio-temporal relations among multiple moving objects. This type of knowledge is usually imprecise, vague and fuzzy, while the reasoning about spatio-temporal relations is intuitive. In this paper we present a model of fuzzy spatio-temporal knowledge representation and reasoning based on high-level Petri nets. The model should be suitable for the design of a knowledge base for real-time, multi-agent-based intelligent systems that include expert or user human-like knowledge. The central part of the model is the knowledge representation scheme called FuSpaT, which supports the representation and reasoning for domains that include imprecise and fuzzy spatial, temporal and spatio-temporal relationships. The scheme is based on the high-level Petri nets called Petri nets with fuzzy spatio-temporal tokens (PeNeFuST). The FuSpaT scheme integrates the theory of the PeNeFuST and 117 spatio-temporal relations.The reasoning in the proposed model is a spatio-temporal data-driven process based on the dynamical properties of the scheme, i.e., the execution of the Petri nets with fuzzy spatio-temporal tokens. An illustrative example of the spatio-temporal reasoning for two agents in a simplified robot-soccer scene is given.