基于遗传算法的模糊PI控制器

模糊PI控制器具有鲁棒性强、控制灵活等优点,但是将其应用于纯迟延系统时超调量较大、响应速度慢。针对此提出了一种基于遗传算法的模糊PI控制器,使用遗传算法对模糊逻辑系统参数进行训练。在以往的模糊逻辑系统建立过程中,主要依靠专家知识或工作人员经验来确定其主要参数（如模糊推理规则和隶属函数参数等）,而该文利用遗传算法对样本数据进行优化来获取系统参数。在遗传算法中,将推理规则和隶属函数参数的确定结合在一起,从而确定最优的模糊逻辑系统。仿真试验结果表明,由该方法得到的控制器用于纯迟延系统具有响应快,超调量小等优点。     

Design a new mixed expert decision aiding system using fuzzy ELECTRE III method for vendor selection

Any decision process deals with two different concerns as its cornerstones, evaluating the alternatives and ranking them based on their performances. In any decision process, the former phase is usually the premise of the latter one. Alternatives’ evaluation is the concept that largely depends on the experts and their expertise, which increase uncertainty in the decision-making process. In addition to all proposed methods for having the experts’ knowledge as evaluations of the alternatives, utilizing expert decision support systems (EDSS) can be a sensible response to such a need. Having evaluated the alternatives in the first phase of a decision-making process, the second phase of the process deals with the ranking the alternatives based on their performances obtained from the first phase. In this paper, we discuss the architecture of a fuzzy system including both modules, utilizing fuzzy concept for dealing with the uncertainty of the problem. Concerning the problem we had been dealt with, our system comprises a fuzzy evaluation module, which is a fuzzy expert system and an appropriate tool for evaluating the existing alternatives promptly and smoothly, without the imposed time delays by the experts to propose their comments and the uncertainty of such expertise-based comments, and a fuzzy ranking module, which is a fuzzy version of ELECTRE III method ranking the alternatives based on their outranking relations and by considering the existing uncertainty in their performances. This way the final ranking is resulted from an independent fuzzy system, which has considered the existing uncertainty in the evaluations not once but twice. Our proposed system has been applied to a real case of vendor selection process in one of the greatest and the most famous companies in the Iranian oil industry, OIEC, and the results are discussed.     

A real-time decision-making of maintenance using fuzzy agent

In many manufacturing processes, real-time information can be obtained from process control computers and other monitoring devices. However, production control problems are frequently accompanied by certain and uncertain conditions. Problems with uncertainty conditions generally include difficulty in identifying an optimal solution in real-time using conventional mathematical approaches. This study presents a fuzzy logic approach for decision-making of maintenance. Some linguistic variables and rules-of-thumb are used to form the fuzzy logic models, based on the domain experts’ experiences in production line and maintenance department. The historical production data are used to train and tune the fuzzy models. The tuned fuzzy models are then embedded into an internet-based and event-oriented information system as fuzzy agent. The production controller can easily make suitable production control decisions based on the inference results of fuzzy agents to satisfy the quick response requirement.     

模糊交互时态逻辑的模型检测

交互时态逻辑已被广泛应用于开放系统的规范描述,交互时态逻辑的模型检测技术是一个比较重要的验证方法。为了形式化描述和验证具有模糊不确定性信息的开放系统的性质,提出了一种模糊交互时态逻辑,并讨论了它的模型检测问题。首先,引入了模糊交互时态逻辑的基于路径和基于不动点的两种语义,证明了其等价性。然后,基于其等价性,给出了模糊交互时态逻辑的模型检测算法和复杂性分析。     

A type-2 fuzzy logic controller design for buck and boost DC–DC converters

Conventional (type-1) fuzzy logic controllers have been commonly used in various power converter applications. Generally, in these controllers, the experience and knowledge of human experts are needed to decide parameters associated with the rule base and membership functions. The rule base and the membership function parameters may often mean different things to different experts. This may cause rule uncertainty problems. Consequently, the performance of the controlled system, which is controlled with type-1 fuzzy logic controller, is undesirably affected. In this study, a type-2 fuzzy logic controller is proposed for the control of buck and boost DC–DC converters. To examine and analysis the effects of the proposed controller on the system performance, both converters are also controlled using the PI controller and conventional fuzzy logic controller. The settling time, the overshoot, the steady state error and the transient response of the converters under the load and input voltage changes are used as the performance criteria for the evaluation of the controller performance. Simulation results show that buck and boost converters controlled by type-2 fuzzy logic controller have better performance than the buck and boost converters controlled by type-1 fuzzy logic controller and PI controller.     

Experimental validation of a type-2 fuzzy logic controller for energy management in hybrid electrical vehicles

The aim of this paper is to present experimental validation results of an energy management system for hybrid electrical vehicles based on type-2 fuzzy logic. The energy management system (EMS) is designed by extracting knowledge from several experts using surveys. The consideration of interval type-2 fuzzy sets enables modeling the uncertainty in the answers of the experts. The validation of the EMS is performed on a real-scale heavy duty vehicle equipped with different energy sources such as batteries, fuel cell system and ultracapacitors. Experimental results are strong evidence that type-2 fuzzy logic is wide adapted for performing the energy management in hybrid electrical vehicles.     

An inferential approach to Information Retrieval and its implementation using a manual thesaurus

Most inferential approaches to Information Retrieval (IR) have been investigated within the probabilistic framework. Although these approaches allow one to cope with the underlying uncertainty of inference in IR, the strict formalism of probability theory often confines our use of knowledge to statistical knowledge alone (e.g. connections between terms based on their co-occurrences). Human-defined knowledge (e.g. manual thesauri) can only be incorporated with difficulty. In this paper, based on a general idea proposed by van Rijsbergen, we first develop an inferential approach within a fuzzy modal logic framework. Differing from previous approaches, the logical component is emphasized and considered as the pillar in our approach. In addition, the flexibility of a fuzzy modal logic framework offers the possibility of incorporating human-defined knowledge in the inference process. After defining the model, we describe a method to incorporate a human-defined thesaurus into inference by taking user relevance feedback into consideration. Experiments on the CACM corpus using a general thesaurus of English, Wordnet, indicate a significant improvement in the system's performance.     

Fuzzy Non-Homogeneous Markov Systems

In this paper the theory of fuzzy logic and fuzzy reasoning is combined with the theory of Markov systems and the concept of a fuzzy non-homogeneous Markov system is introduced for the first time. This is an effort to deal with the uncertainty introduced in the estimation of the transition probabilities and the input probabilities in Markov systems. The asymptotic behaviour of the fuzzy Markov system and its asymptotic variability is considered and given in closed analytic form. Moreover, the asymptotically attainable structures of the system are estimated also in a closed analytic form under some realistic assumptions. The importance of this result lies in the fact that in most cases the traditional methods for estimating the probabilities can not be used due to lack of data and measurement errors. The introduction of fuzzy logic into Markov systems represents a powerful tool for taking advantage of the symbolic knowledge that the experts of the systems possess.     

Fuzzy expert systems and challenge of new product pricing

This paper is focused on the representation and treatment of knowledge and data uncertainty within the context of an important industrial challenge, i.e., new product pricing. The most well known participating factor in pricing process is cost meanwhile the other factors like customer value and firm’s strategy should be considered in the pricing process, as well. Besides, there are other important factors like the risks that consumer bear in purchasing new product which must be carefully analyzed and considered. Nonetheless, many of these factors are blended with uncertainty. In recent decades, fuzzy logic was well developed and implemented in many applications to treat vagueness in complicated systems. Finding the pricing process a critical and complicated process which includes many vague parameters, we tried to design a fuzzy expert system to cope with this challenge. In this paper, after a brief introduction of fuzzy logic which has revealed a methodology to work with uncertainty and imitate humans reasoning, the pricing factors are introduced. Then a fuzzy expert system is designed to find the appropriate price of the new product considering the related parameters.     

多智体系统时序认知规范的SPIN模型检测

SPIN模型检测器主要用来检测线性时序逻辑描述的规范,而多智体系统的规范采用时序认知逻辑描述比较方便。本文着重讨论了如何利用SPIN模型检测线性时序认知逻辑的方法,根据局部命题的理论,将模型检测知识算子和公共算子表述的规范规约为模型检测线性时序逻辑的问题,从而使SPIN的检测功能由线性时序逻辑扩充到线性时序认知逻辑。本文通过一个RPC协议分析实例来说明模型检测线性时序认知逻辑的方法。     

Using B-spline neural network to extract fuzzy rules for a centrifugal pump monitoring

In mechanical equipment monitoring tasks, fuzzy logic theory has been applied to situations where accurate mathematical models are unavailable or too complex to be established, but there may exist some obscure, subjective and empirical knowledge about the problem under investigation. Such kind of knowledge is usually formalized as a set of fuzzy relationships (rules) on which the entire fuzzy system is based upon. Sometimes, the fuzzy rules provided by human experts are only partial and rarely complete, while a set of system input/output data are available. Under such situations, it is desirable to extract fuzzy relationships from system data and combine human knowledge and experience to form a complete and relevant set of fuzzy rules. This paper describes application of B-spline neural network to monitor centrifugal pumps. A neuro-fuzzy approach has been established for extracting a set of fuzzy relationships from observation data, where B-spline neural network is employed to learn the internal mapping relations from a set of features/conditions of the pump. A general procedure has been setup using the basic structure and learning mechanism of the network and finally, the network performance and results have been discussed.     

Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework

Traditional approaches for software projects effort prediction such as the use of mathematical formulae derived from historical data, or the use of experts judgments are plagued with issues pertaining to effectiveness and robustness in their results. These issues are more pronounced when these effort prediction approaches are used during the early phases of the software development lifecycle, for example requirements development, whose effort predictors along with their relationships to effort are characterized as being even more imprecise and uncertain than those of later development phases, for example design. Recent works have demonstrated promising results using approaches based on fuzzy logic. Effort prediction systems that use fuzzy logic can deal with imprecision; they, however, can not deal with uncertainty. This paper presents an effort prediction framework that is based on type-2 fuzzy logic to allow handling imprecision and uncertainty inherent in the information available for effort prediction. Evaluation experiments have shown the framework to be promising.     

模糊时间Petri网的时间推理及其在过程监测中的应用

针对传统分析方法的不足,提出用线性逻辑给出模糊时间Petri网描述和时间推理的方法。该方法能清楚地分析模糊时间Petri网的运行行为,具体例子说明了其在系统过程监测和诊断中的应用。     

A new expert system for learning management systems evaluation based on neutrosophic sets

There has been a sudden increase in the usage of Learning Management Systems applications to support learner's learning process in higher education. Many studies in learning management system evaluation are implemented under complete information, while the real environment has uncertainty aspects. As these systems were described by development organizations with uncertainty terms such as vague, imprecise, ambiguity and inconsistent, that is why traditional evaluation methods may not be effective. This paper suggests neutrosophic logic as a better option to simulate human thinking than fuzzy logic because unlike fuzzy logic, it is able to handle indeterminacy of information which expresses the percentage of unknown parameters. As previous studies suggested neutrosophic decision maker and neutrosophic expert systems as future work in ecommerce and e‐learning applications, this paper presents neutrosphic expert system for learning management systems evaluation. Information for building and validating the neutrosophic expert system is collected from five experts using surveys, and then analysis is done by using Fuzzytech 5.54d software. Finally, the comparison between fuzzy expert system and neutrosophic expert system results show that the neutrosophic logic is capable of representing uncertainty in human thinking for evaluating Learning Management Systems.     

Designing an intelligent ontological system for traffic light control in isolated intersections

This paper models the traffic light control domain using a fuzzy ontology and applies it to control isolated intersections. Proposing an independent module for reusing traffic light control knowledge is one of the most important purposes of this paper. In this way, software independency increases and other software development activities, such as test and maintenance, are facilitated. The ontology has been developed manually and evaluated by experts. Moreover, the traffic data is extracted and classified from images of intersections using image processing algorithms and artificial neural networks. According to predefined XML schema, this information is transformed to XML instances and mapped onto the fuzzy ontology for firing suitable fuzzy rules using a fuzzy inference engine. The performance of the proposed system is compared with other similar approaches. The comparison shows that it has a much lower average delayed time for each car in each cycle in all traffic conditions as compared with the other ones.     

Fuzzy branching temporal logic

Intelligent systems require a systematic way to represent and handle temporal information containing uncertainty. In particular, a logical framework is needed that can represent uncertain temporal information and its relationships with logical formulae. Fuzzy linear temporal logic (FLTL), a generalization of propositional linear temporal logic (PLTL) with fuzzy temporal events and fuzzy temporal states defined on a linear time model, was previously proposed for this purpose. However, many systems are best represented by branching time models in which each state can have more than one possible future path. In this paper, fuzzy branching temporal logic (FBTL) is proposed to address this problem. FBTL adopts and generalizes concurrent tree logic (CTL*), which is a classical branching temporal logic. The temporal model of FBTL is capable of representing fuzzy temporal events and fuzzy temporal states, and the order relation among them is represented as a directed graph. The utility of FBTL is demonstrated using a fuzzy job shop scheduling problem as an example.     

Fuzzy supervision and application to lean production†

A novel philosophy of process supervision based on functional redundancy, i.e., analytical or knowledge based redundancy which may specifically be used for lean production, is suggested. The key idea is to replace the conventional residual evaluator of the fault diagnosis system based on crisp logic, by both a decision maker with fuzzy logic for residual pre-evaluation and the human operator to make the final decisions using his natural intelligence, experience and common sense. The purpose of the employment of fuzzy logic for residual pre-evaluation is to release only weighted alarms instead of yes-no decisions, so that (by definition) no false alarms can be produced; besides this, the man-machine interaction becomes much easier. In contrast to the conventional expert system approach, the proposed concept leaves the final yes-no decisions to the natural intelligence, capability and responsibility of the human operator which are still superior to the artificial intelligence and decision making capabilities of an expert system.     

Evaluating an intelligent diagnosis system of historical text comprehension

This work aims to present and evaluate a Fuzzy-Case Based Reasoning Diagnosis system of Historical Text Comprehension. The synergism of fuzzy logic and case based reasoning techniques handles the uncertainty in the acquisition of human expert's knowledge regarding learner's observable behaviour and integrates the right balance between expert's knowledge described in the form of fuzzy sets and previous experiences documented in the form of cases. The formative evaluation focused on the comparison of the system's performance to the performance of human experts concerning the diagnosis accuracy. The system was also evaluated for its behaviour when using two different historical texts. Empirical evaluation conducted with human experts and real students indicated the need for revision of the diagnosis model. The evaluation results are encouraging for the system's educational impact on learners and for future work concerning an intelligent educational system for individualized learning.     

Process situation assessment: From a fuzzy partition to a finite state machine

Process situation assessment plays a major role in supervision of complex systems. The knowledge of the system behavior is relevant to support operators in their decision tasks. For complex industrial processes such as chemical or petrochemical ones, most of supervision approaches are based on data acquisition techniques and specifically on clustering methods to cope with the difficulty of modeling the process. Consequently, the system behavior can be characterized by a state space partition. This way, situation assessment is performed online through the tracking of the system evolution from one class to another. Furthermore, a finite state machine that is a support tool for process operators is elaborated to model the system behavior. This article presents theoretical aspects according to which the intuition that the trajectory observation of a dynamical system by a sequence of classes, to which the actual state belongs, gives valuable information about the real behavior of the system is substantiated. Thus, practical aspects are developed on the state machine construction and illustrated by two simple applications in the domain of chemical processes.