Knowledge Representation Using High-Level Fuzzy Petri Nets

This correspondence presents a high-level fuzzy Petri net (HLFPN) model to represent the fuzzy production rules of a knowledge-based system, where a fuzzy production rule is the one that describes the fuzzy relation between the antecedent and the consequent. The HLFPN can be used to model fuzzy IF-THEN rules and IF-THEN-ELSE rules, where the fuzzy truth values of the propositions are restricted to [0, 1]. Based on the HLFPN model, an efficient algorithm is proposed to automatically reason about imprecise and fuzzy information. In this correspondence, a novel model to represent fuzzy knowledge is developed. When compared with other related models, the HLFPN model preserves several significant advantages. Finally, main results are presented in the form of eight properties and are supported by a comparison with other existing algorithms     

模糊Petri网及其在Web服务信任评估中的应用*

为了提高 Web服务的信任评估准确度和交互成功率,提出了一种新的基于模糊Petri网的Web服务信任评估模型(WS-TEM)。该模型给出了评估的模糊信任规则,在此基础上,提出了一种基于模糊Petri网的推理算法,并使用模糊Petri网中知识表示和运行的基本算法,并行处理一个服务实体的多个推荐信任来综合评估Web服务实体间的信任关系。经过模拟实验和分析表明,该模型在Web服务信任评估上更加准确可靠,从而降低了交互的风险,保证了交互的成功率。     

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     

应用带标识的模糊Petri网的知识表示方法

提出一种在某些库所中带有标识的模糊Petri网模型来进行知识表示。为了获得更多的加权模糊产生式规则的信息,在知识表示的过程中考虑了权值,确定性因子,阈值等参数。这种模糊Petri网充分利用了Petri网的并行处理能力。随着带标识的模糊Petri网的运行,网中标识的变化可以标记加权模糊推理的运行。通过文中给出的基于相似性测度的计算方法可以更加高效地计算出多层加权模糊推理的推理结果。     

基于直觉模糊Petri网的故障诊断

故障诊断经常受到多种不确定性和模糊性因素的影响,针对不确定性的故障诊断问题,利用直觉模糊集较好的表达不确定性信息的优势和Petri网较好的并行处理以及图形处理问题的能力,构建了直觉模糊Petri网模型。由于将直觉模糊推理转化为矩阵运算的过程中有非隶属度参数的参与,因此推理结果可提供更多的信息。根据实际故障诊断中的模糊推理问题,给出了带有权值、阈值等参数条件下新的直觉模糊推理算法。通过获取和处理故障诊断中的不确定性和模糊性的知识,该算法将故障诊断过程转化为利用直觉模糊Petri网的直觉模糊推理过程。实际燃气轮机故障诊断模型案例表明了所给直觉模糊推理算法的有效性。     

Reinforcement learning for high-level fuzzy Petri nets

The author has developed a reinforcement learning algorithm for the high-level fuzzy Petri net (HLFPN) models in order to perform structure and parameter learning simultaneously. In addition to the HLFPN itself, the difference and similarity among a variety of subclasses concerning Petri nets are also discussed. As compared with the fuzzy adaptive learning control network (FALCON), the HLFPN model preserves the advantages that: 1) it offers more flexible learning capability because it is able to model both IF-THEN and IF-THEN-ELSE rules; 2) it allows multiple heterogeneous outputs to be drawn if they exist; 3) it offers a more compact data structure for fuzzy production rules so as to save information storage; and 4) it is able to learn faster due to its structural reduction. Finally, main results are presented in the form of seven propositions and supported by some experiments.     

A fuzzy Petri net-based expert system and its application to damageassessment of bridges

In this paper, a fuzzy Petri net approach to modeling fuzzy rule-based reasoning is proposed to bring together the possibilistic entailment and the fuzzy reasoning to handle uncertain and imprecise information. The three key components in our fuzzy rule-based reasoning-fuzzy propositions, truth-qualified fuzzy rules, and truth-qualified fuzzy facts-can be formulated as fuzzy places, uncertain transitions, and uncertain fuzzy tokens, respectively. Four types of uncertain transitions-inference, aggregation, duplication, and aggregation-duplication transitions-are introduced to fulfil the mechanism of fuzzy rule-based reasoning. A framework of integrated expert systems based on our fuzzy Petri net, called fuzzy Petri net-based expert system (FPNES), is implemented in Java. Major features of FPNES include knowledge representation through the use of hierarchical fuzzy Petri nets, a reasoning mechanism based on fuzzy Petri nets, and transformation of modularized fuzzy rule bases into hierarchical fuzzy Petri nets. An application to the damage assessment of the Da-Shi bridge in Taiwan is used as an illustrative example of FPNES.     

基于知识Petri网的确定性和不确定性联合推理

知识推理是人工智能的核心领域,旨在研究如何从已知(知识库和推理规则)推理出未知,以帮助智能体做出科学决策.而智能体所处的环境存在不可观性和不确定性,因此知识库通常不仅包含确定性知识,还包含不确定性知识,而且推理过程需要两类知识紧密协作.然而,目前的推理方法无法将两类知识统一表示,常常将两者对应的推理过程割裂进行.基于此,为了实现在统一的模型架构下完成确定性和不确定性联合推理,给出了一种知识Petri网推理方法.首先,定义了一种新的知识Petri网,使其不仅能够描述确定性的知识规范,也可以描述先验概率知识;其次,根据知识Petri网的网结构,给出了一种知识Petri网概率独立剪枝算法,能够指数级地降低不确定性推理的计算复杂性;最后,利用知识Petri网及其概率独立剪枝算法,给出了一种新型推理算法,实现了确定性和不确定性的联合推理,并利用Wumpus世界进行了演示和验证.     

模糊Petri网及其在模糊推理中的应用

该文首先引入模糊Petri网(Fuzzy Petri Net)的定义，给出了一个九元模糊Petri网模型，并且详细介绍了模糊Petri网的激发规则。给出了产生式规则的模糊Petri网表示的三种模型，在此基础上提出了一种基于模糊Petri网的推理方法，最后使用模糊Petri网中知识表示和运行的基本算法解决了汽车质量检验和故障分析这一实际问题。实际应用证明基于模糊Petri网的产生式规则的推理具有知识表达能力强，处理不确定知识正确，推理过程简单直观，具有一定的智能推理能力，具有较强的实用价值。     

Modeling, scheduling and simulation of product development process by extended stochastic high-level evaluation Petri nets

This paper presents a methodology for modeling and simulating product development process-based on the extended stochastic high-level evaluation Petri nets (ESHLEP-N). A product development process is composed of many design activities and the ESHLEP-N model can describe some special features of design activity in detail, such as randomness of its duration, uncertainty of its interruption and complexity of design iteration. Therefore, the ESHLEP-N model is employed to simulate a product development process. The initial product development plan obtained by a mathematical method beforehand is taken as the input of the simulation. Then the simulation procedure is proposed, along with four types of rules, i.e. activity-sequencing rules, resource-assigning rules, state-changing rules and the simulation-terminating rule, for scheduling the design activities. An example of the development process of an automobile drive system in concurrent engineering environment is presented to illustrate the method of the ESHLEP-N-based modeling, simulation procedure and scheduling rules. The simulation results show that the simulation procedure and the scheduling rules are effective.     

A self-organizing fuzzy logic controller for dynamic systems usinga fuzzy auto-regressive moving average (FARMA) model

The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules, however, the proposed new fuzzy logic controller needs no expert in making control rules, Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are stored in the fuzzy rule space and updated online by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum     

一种改进的基于规则的带权模糊推理算法

给出了一种基于带权模糊Petri网的模糊推理算法，这种算法可以适用于一类基于规则的系统，即可以对该类系统的带权模糊Petri网模型进行相应的处理。对系统建立了知识表示的带权模糊Petri网模型，使用该算法可以确定用户给定的库所之间是否存在联系，即相应的命题之间是否存在因果关系，可以计算出命题的模糊真值。对具体的算例进行了分析并与已有的算法进行了比较，得出该算法的优点。     

一种新的递阶多变量模糊控制器的设计

针对多变量模糊控制器设计中存在规则库维数过高、隶属函数以及规则库难于获取的问题,提出一种新的递阶模糊控制器（NHFLC）,使得总的规则数大大减少,且无需了解各输入变量对系统的影响程度,基于该控制器结构下的模糊控制策略利用遗传算法来获得,仿真结果表明NHFLC与通常的MIMO模糊控制器相比,具有更好的动态性能和鲁棒稳定性。     

基于加权FPN的聚驱综合调整系统决策分析

模糊Petri网(Fuzzy Petri Nets, FPN)是一种适合于描述异步并发事件的计算机系统模型,可以有效地对并行和并发系统进行形式化验证和决策分析.针对聚驱综合调整系统知识具有不确定性和模糊性的特点,给出了基于加权模糊产生式规则的加权FPN决策模型.在此模型的基础上,给出了决策推理过程的形式化推理算法.算法考虑了推理过程中的众多约束条件,将复杂的推理过程采用矩阵运算来实现,充分利用了FPN的并行处理能力,使决策推理过程更加简单和快速.并以压裂方式调整为例,说明了该模型具有直观、表达能力强和易于推理等优点,具有较强的实用价值.     

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.     

基于专家系统和Petri网的网络可靠性评估方法

针对网络系统可靠性建模难的问题,结合专家系统和Petri网理论各自的特点,提出了一种基于知识库系统的Petri网可靠性评估方法。首先引入模糊神经Petri网的定义和适用于网络系统可靠性建模的引发规则,然后在此基础上提出一种学习算法,其既可以表示和处理模糊产生式规则的知识库系统,又具有学习能力。最后以一个无向网络的可靠性评估为例,并通过定性分析和定量计算,验证了算法的合理性和有效性。     

基于规则推理的FPN误用入侵检测方法

针对网络入侵攻击活动的模糊性，提出了一种基于模糊推理的模糊Petri网（FPN）误用入侵检测方法。该方法定义了一个六元组FPN，并将模糊产生式规则精化为两种基本类型。在此基础上给出了FPN表示模糊规则的模型、推理过程和基于FPN的推理算法。最后通过入侵检测的实例对该方法的正确性和有效性进行了验证，结果表明该方法推理过程简单直观、容易实现，而且具有并行推理能力，可适用于大规模的FPN模型，是误用入侵检测技术的一种非常有效的解决方案。     

基于共识与Choquet积分的模糊Petri网可信度确定方法*

针对模糊Petri网可信度确定过程中,决策结果可接受程度较低和未考虑影响因子间关联的问题,提出基于群体共识测度与Choquet积分确定模糊Petri网可信度的方法。设计了基于决策者与群体评估信息偏差修正的自适应共识测度算法,获取可接受程度较高的群体评估信息。构建最大2-可加模糊测度Marichal熵模型求解影响因子间的交互作用系数,根据交互作用系数、默比乌斯变换和模糊测度之间的关系确定模糊测度,利用Choquet积分算子集结群体评估信息得到模糊Petri网可信度。将此方法用于确定燃气轮机故障诊断模糊Petri网输入库所可信度,并与未考虑共识测度和影响因子间关联的方法进行比较,计算结果验证了该方法的有效性和可行性。     

时间Petri网等效变换的工作流评价

时间性能分析是工作流模型分析和评价的重要方面.首先介绍了业务过程的一般Petri网模型,然后引入时间Petri网建立工作流网的时间约束模型.提出了保持网时间约束特性不变的网变换规则和时间性能计算方法,对工作流网进行化简及时间性能计算,并给出了相应的算法.最后通过一个实例说明本方法的应用过程.     

优势模糊粗糙模型及其在审计风险评估中的应用

针对模糊信息系统,建立基于优势关系的粗糙集模型,并给出该模型的多种知识约简定义.通过构造分辨函数,得到了求解优势模糊粗糙集模型的最大分布约简算法.最后将该模型应用于审计风险评估,得到了较为合理的评估规则.