A Fuzzy Petri Nets approach for railway traffic control in case of abnormality: Evidence from Taiwan railway system

Railway traffic control by dispatchers in case of abnormality is critical to assure the service quality of a railway system’s operation. However, this unique professional knowledge often lies in the dispatcher’s mind. Therefore, this study aims to transform a train dispatcher’s expertise into a useful knowledge rule. The fuzzy Petri Net approach is adopted to formulate the decision rules of train dispatchers in case of abnormality as the basis for future development of a dispatching decision support system. The dispatching decision rules, factors, and possible options when perturbation happens are collected via expert interviews and literature reviews. This study discusses the abnormal scenarios, including centralized traffic control system failure, automatic train protection failure, and locomotive failure. A case study of a line section of Taiwan’s railway network is implemented and the empirical result could be used as a reference in railway dispatching in case of abnormality.     

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     

A process-control and diagnostic tool based on continuous fuzzy Petri nets

The use of Petri nets and fuzzy logic in intelligent process control has caught the attention of many researchers. In this paper, a Continuous Fuzzy Petri Net (CFPN) tool which integrates the three technologies of fuzzy control, Petri nets and real-time expert systems is presented. The CFPN approach can deal with real-time continuous inferencing, for the purpose of process monitoring and diagnostics, at a high level in the presence of uncertainty. This tool has been implemented in the G2 real-time expert-system environment and is currently being used by ESSO Canada.     

A development environment for fuzzy rule-based traffic control

We present a development environment for fuzzy rule-based traffic control systems. This Macintosh® environment allows a user to simulate traffic on a network of streets and create fuzzy decision rules for adjusting traffic signal timing parameters at the intersections. During simulation, various graphical displays help the user interactively debug and fine-tune the decision rules. When the results are satisfactory, C or Ada code can be automatically generated to implement the fuzzy knowledge base. We also describe a specific knowledge base developed using this environment and present some results.     

Some applications of fuzzy logic in rule–based expert systems

Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts' opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems.     

DECISION SUPPORT WITH LOGICAL AND FUZZY PETRI NETS

Modern knowledge representation is a very dynamic domain because of continuous research and development. This paper presents Logical Petri Nets (LPNs) and Fuzzy Petri Nets (FPNs) as models for knowledge representation. It is shown that knowledge propagation, described using logical and fuzzy Petri nets, terminates in a unique stable state. Based on this result, the paper introduces an algorithm for knowledge propagation in decision support systems.     

A fuzzy Petri net model to estimate train delays

Even with the most accurate timetable, trains often operate with delays. The running and waiting times for trains can increase unexpectedly, creating primary delays that cause knock-on delays and delays for other trains. The accurate estimation of train delays is important for creating timetables, dispatching trains, and planning infrastructures. In this work, we proposed a fuzzy Petri net (FPN) model for estimating train delays. The FPN model with characteristics of hierarchy, colour, time, and fuzzy reasoning was used to simulate traffic processes and train movements in a railway system. The trains were coloured tokens, the track sections were termed places, and discrete events of train movement were termed transitions. The train primary delays were simulated by a fuzzy Petri net module in the model. The fuzzy logic system was incorporated in the FPN module in two ways. First, when there were no historical data on train delays, expert knowledge was used to define fuzzy sets and rules, transforming the expertise into a model to calculate train delays. Second, a model based on the Adaptive Network Fuzzy Inference System (ANFIS) was used for systems where the historical data on train delays were available (from detection systems or from the train dispatcher’s logs). The delay data were used to train the neuro-fuzzy ANFIS model. After the results of the fuzzy logic system were verified, the ANFIS model was replicated by a fuzzy Petri net. The simulation was validated by animating the train movement and plotting the time-distance graph of the trains. Results of the simulation were exported to a database for additional data mining and comparative analysis. The FPN model was tested on a part of the Belgrade railway node.     

模糊Petri网在非结构化决策支持中的应用研究

提出了一种建立非结构化决策支持的模糊Petri网模型（简称XFPN).CXFPN结合了模糊集合理论和Petri网理论的基本特点,是对基本模糊Petri网的进一步扩充。文中以促销决策支持为例,描述了非结构化决策问题到CXFPN模型建立,以及决策支持推导的相关问题。应用这种模型,对一类基于规则的决策系统的设计、分析和维护等变得容易了。     

基于自学习模糊Petri网的知识化制造系统采购预测

对模糊Petri网进行改进,使其增加自学习能力,即自学习模糊Petri网(SFPN).提出了自学习模糊Petri网模型知识库的建立方法,通过构造SFPN模型知识库,建立并保存现有产品的SFPN模型,开发新产品或进行新的决策时调出并进行修正后作为新产品模型.通过较短时间和少量样本的自学习训练,便可用于新产品的预测或决策.最后通过采购预测实例验证了该方法的有效性.     

Reliability analysis of waste clean-up manipulator using genetic algorithms and fuzzy methodology

In this paper, the reliability analysis of waste clean-up manipulator has been performed using Real Coded Genetic Algorithms and Fuzzy Lambda Tau Methodology. The optimal values of mean time between failures and mean time to repair are obtained using genetic algorithms. Petri Net tool is applied to represent the interactions among the working components of the system. To enhance the relevance of the reliability study, triangular fuzzy numbers are developed from the computed data, using possibility theory. The use of fuzzy arithmetic in the Petri Net model increases the flexibility for application to various systems and conditions. Various reliability parameters (failure rate, repair time, mean time between failures, expected no. of failures, reliability and availability) are computed using Fuzzy Lambda Tau Methodology. Sensitivity analysis has also been performed and the effects on system mean time between failures are addressed. The adopted methodology improves the shortcomings/drawbacks of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation.     

基于FPN—RSVM的电梯故障诊断方法

模糊Petri网(FPN)作为产生式规则系统的建模、知识表示和诊断推理的工具,在故障诊断领域得到了广泛的应用。但调整学习机制的缺乏使其不能处理专家系统的变化。使用回归型支持向量机(RSVM)对电梯在不同运行状态下的专家诊断数据进行训练,可以得到与电梯状态相对应的FPN诊断网络权值。故障发生时,根据电梯运行状态选择相应的权值,可以使得诊断结果更加准确,更加符合实际情况。     

基于WFPN的PAAIS知识表示及校验研究

驾驶员辅助决策人工智能系统(PAAIS)是辅助飞机驾驶员进行决策的智能系统,其核心是知识处理。以加权模糊Petri网(WFPN)表示PAAIS知识库中模糊产生式规则,并提出有效的模糊推理算法。针对PAAIS具有多个领域知识库的特点,提出基于WFPN的多知识库校验算法。     

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.     

直觉模糊Petri网的空战战术决策

空战对抗时间短、任务重、态势变化迅速、不确定性因素多。直觉模糊集是处理不确定性问题的有效方法,结合Petri网强大的知识表示能力,给出了直觉模糊Petri网（IFRN）的模糊推理算法和推理规则。针对空战战机稍纵即逝的特点,适当选取模糊控制变量和隶属度函数,构建了直觉模糊Petri网决策模型。选取某空战实例进行仿真实验,决策结果与专家的预测结果吻合,验证了模型的正确性和可用性。分析了模型在实际应用中存在的缺陷,提出适当的改进意见,使模型更加完善。     

模糊Petri网的反馈控制研究

本文首先将模拟技术应用于Ｐｅｔｒｉ网中，得到模糊Ｐｅｔｒｉ网模型，然后基于Ｐｅｔｒｉ网中的库所湾量的概念，在普通Ｐｅｔｒｉ网的反馈控制基础上提出了一种模型Ｐｅｔｒｉ网的反馈控制方法。该方法使得对复杂系统的模糊Ｐｅｔｒｉ多控制器的系统设计成为可能。     

The Need for Fuzzy AI

Artificial intelligence (AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by uncertainty. Fuzzy systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is permitted. This paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including variability. In conclusion, this paper argues for the need for " fuzzy AI” in two senses: (i) the need for fuzzy methodologies (in the technical sense of Zadeh’s fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and (ii) the need for fuzziness (in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.      

基于模糊微分Petri网的混合系统表示

为了描述连续部分为模糊的混合系统,提出了基于模糊微分Petri网的混合系统表示方法。该方法的切换规则由时间来控制并由离散变迁来监督管理,而连续部分则由模糊微分方程描述,通过模糊微分库所和模糊微分变迁来建模。最后通过实例来展示我们的Petri网对混合系统的表示能力。     

一种新的模糊Petri网推理机制

针对模糊Petri网(FPN)建立过程中模糊产生式规则各项参数的确定问题,通过引入一种新的FPN推理机制,利用虚库所和虚变迁构建分层FPN模型。该方法的实现不依赖经验数据,对初始输入无严格要求。仿真实例结果表明,利用该推理机制对非训练样本中的输入数据进行模糊推理,所得的FPN模型具有较强的泛化和自适应能力。     

A Strategy of Dynamic Reasoning in Knowledge-Based System with Fuzzy Production Rules

Fuzzy production rules have been successfully applied to represent uncertainty in a knowledge-based system. The knowledge organized as a knowledge base is static. On the other hand, a real system such as the stock market is dynamic in nature. Therefore we need a strategy to reflect the dynamic nature of a system when we make reasoning with a knowledge-based system.This paper proposes a strategy of dynamic reasoning that can be used to takes account the dynamic behavior of decision-making with the knowledge-based system consisted of fuzzy rules. A degree of match (DM) between actual input information and antecedent of a rule is represented by a value in interval [0, 1]. Weights of relative importance of attributes in a rule are obtained by the AHP (Analytic Hierarchy Process) method. Then these weights are applied as exponents for the DM, and the DMs in a rule are combined, with the Min operator, into a single DM for the rule. In this way, the importance of attributes of a rule, which can be changed from time to time, can be reflected to reasoning in knowledge-based system with fuzzy rules.With the proposed reasoning procedure, a decision maker can take his judgment on the given decision environment into a static knowledge base with fuzzy rules when he makes decision with the knowledge base. This procedure can be automated as a pre-processing system for fuzzy expert systems. Thereby the quality of decisions could be enhanced.     

基于结果反馈的模糊Petri网学习算法

针对模糊Petri网模型的复杂结构,在不增加虚库所和虚变迁的情况下改进了模糊Petri网分层算法,从而简化模糊Petri网学习和训练方法。为提高收敛速率,本文从一个全新的角度考虑模糊Petri网的学习和训练,提出了基于结果反馈的模糊Petri网学习的新算法（FBFPN）。该算法通过对纯网进行层次式分层及建立变迁点燃的近似连续函数后,调整权值、变迁的阈值、变迁的可信度的同时又调整输入矢量的多重作用来最小化误差函数。仿真结果分析表明,该算法具有良好的学习效率和泛化能力。