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.     

Fuzzy set based crack diagnosis system for reinforced concrete structures

This paper presents a computer assisted crack diagnosis system for reinforced concrete structures which aids the non-expert to diagnose the cause of cracks at the level of an expert in the general inspection of structures. The system presented adapts fuzzy set theory to reflect fuzzy conditions, both for crack symptoms and characteristics which are difficult to treat using crisp sets. The inputs to the system are mostly linguistic variables concerning the crack symptoms and some numeric data about concrete and environmental conditions. Using these input data and based on built-in rules, the proposed system executes fuzzy inference to evaluate the crack causes under consideration. The built-in rules were constructed by extracting expert knowledge, primarily from technical books about concrete and concrete cracks. We implemented the proposed system in a computer program with a graphic user interface for actual utilization in practical business fields. When applied to cracks actually diagnosed by experts, the proposed system provided results similar to those obtained by experts, and we expect that this system can be used as an effective crack diagnosis tool for both experts and non-experts in the regular inspection of RC structures.     

A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques

In this research work, a novel framework for the construction of augmented Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction methods for decisions in medical informatics is investigated. Specifically, the issue of designing augmented Fuzzy Cognitive Maps combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods is explored. Fuzzy cognitive maps are knowledge-based techniques which combine elements of fuzzy logic and neural networks and work as artificial cognitive networks. The knowledge extraction methods used in this study extract the available knowledge from data in the form of fuzzy rules and insert them into the FCM, contributing to the development of a dynamic decision support system. The fuzzy rules, which derived by these extraction algorithms (such as fuzzy decision trees, association rule-based methods and neuro-fuzzy methods) are implemented to restructure the FCM model, producing new weights into the FCM model, that initially structured by experts. Concluding, our scope is to present a new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods. A well known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection is presented to illustrate the application of the proposed framework and its functioning.     

设计型专家系统在机械工程中的应用研究

专家系统是人工智能技术的一个重要分支，它是特定领域的一套计算机程序，具有类似专家工作时利用知识进行推理来解决问题的能力。它一般用以求解那些需要人类专家才能求解的高难度问题或不良结构的问题，为人类保存、使用、传播和评价知识提供了一条有效的捷径。文中主要介绍设计型专家系统在机械工程中的应用以及其基本结构、知识表示方法、推理方式及构建策略，然后介绍了它在齿轮传动设计中的应用。设计型专家系统的产生和发展必然会促进设计自动化技术在机械工程中的应用。     

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.     

Development and Evaluation of a Fuzzy Inference System and a Neuro-Fuzzy Inference System for Grading Apple Quality

In this research work, a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) were developed to classify apple total quality based on some fruit quality properties, i.e., fruit mass, flesh firmness, soluble solids content and skin color. The knowledge from experts was used to construct the FIS in order to be able to efficiently categorize the total quality. The historical data was used to construct an ANFIS model, which uses rules extracted from data to classify the apple total quality. The innovative points of this work are (i) a clear presentation of fruit quality after aggregating four quality parameters by developing a FIS, which is based on experts’ knowledge and next an ANFIS based on data, and (ii) the classification of apples based on the above quality parameters. The quality of apples was graded in five categories: excellent, good, medium, poor and very poor. The apples were also graded by agricultural experts. The FIS model was evaluated at the same orchard for data of three subsequent years (2005, 2006 and 2007) and it showed 83.54%, 92.73% and 96.36% respective average agreements with the results from the human expert, whereas the ANFIS provided a lower accuracy on prediction. The evaluation showed the superiority of the proposed expert-based approach using fuzzy sets and fuzzy logic.     

关系数据库中模糊规则的快速挖掘算法

关联规则和时序规则是数据挖掘的任务之一.在以往的算法中,规则通常用确定的数值或概念来表示,往往不具有实际意义,而且不容易被用户理解.研究了从大型关系数据库中挖掘模糊关联规则和模糊时序规则的问题.基于模糊集合的理论,提出了两个模糊关联规则的挖掘算法,然后把它们分别扩展为模糊时序规则的挖掘算法.用模糊概念表示的规则更符合人的思维和表达习惯,增强了规则的可理解性.     

Reinforcement learning for facilitating human-robot-interaction in manufacturing

For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes remains high, and as a consequence, the number of interactions between humans and robots has increased in this context. This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. Despite the natural human aptitude for flexibility, their presence remains a source of disturbance within the system and make modelling and optimization of these systems considerably more challenging, and in many cases impossible. Improving the ability of robotic operators to adapt their behaviour to variations in human task performance is, therefore, a significant challenge to be overcome to enable many ideas in the larger intelligent manufacturing paradigm to be realised. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. This decision-making provides the robotic operators with greater adaptability, by enabling its behaviour to change based on observed information, both of its environment and human colleagues. The work extends theoretical knowledge on how learning methods can be implemented for robotic control, and how the capabilities that they enable may be leveraged to improve the interaction between robots and their human counterparts. The work further presents a novel methodology for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues. The development and evaluation are supported by a generalized simulation model, which is parameterized to enable appropriate variation in human performance. The evaluation demonstrates that the reinforcement agent can effectively learn to make adjustments to its behaviour based on the knowledge extracted from observed information, and balance the task demands to optimise these adjustments.     

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.     

基于Web的通用疾病计算机辅助诊断平台

针对传统医疗专家系统设计的复杂性和局限性,提出一种基于Web的通用疾病辅助诊断平台。阐述系统的整体架构和工作流程,讨论该解决方案的设计思想及实现方法。介绍数据规范化、病历模板、病历采集、知识获取、在线诊断功能的实现技术。实际测试表明,该系统在功能和性能上都是可行的。     

Towards true linguistic modelling through optimal numerical solutions

This paper is concerned with both the problems of quantitative and qualitative modelling of complex systems by using fuzzy techniques. A unified approach for the identification and subsequent extraction of linguistic knowledge of systems using fuzzy relational models is addressed. This approach deals with the identification problem by means of optimal numerical solutions based on weighted least squares and quadratic programming formulations. The linguistic knowledge is extracted in the form of consistent fuzzy rules that describe linguistically the behaviour of the identified system. A new methodology for the simplification of the extracted rules is derived by using a pruning criterion based on the representability matrix concept introduced in previous work. Several numerical aspects concerning the proposed optimization schemes and a covering discussion about the linguistic interpretation of the resulting models are also included together with illustrative examples in the contexts of pattern classification and dynamic systems identification. The paper also provides an overview of fuzzy modelling techniques that intends to situate the relational models among other fuzzy model architectures typically adopted in the literature, highlighting their main advantages and drawbacks.     

The knowledge elicitation problem: a psychological perspective

The knowledge elicitation problem arises from the need to acquire the knowledge of human experts in an explicit form suitable for encoding in a computer program such as an expert system. This is very difficult to perform successfully because of the size and complexity of knowledge structures in the human brain, and because much procedural knowledge is tacit and unavailable to conscious verbal report via interview methods. The present paper draws upon an extensive review of research in the field of cognitive psychology in an attempt to offer a practical approach to this problem. First, a wide range of cognitive theories concerning the nature of knowledge representation in humans is considered, and a synthesis of the current state of theory is provided. Second, attention is drawn to a number of performance factors which may constrain the exhibition of a person's underlying cognitive competence. There then follows a review and discussion of a number of alternative psychological methodologies that might be applied to the elicitation of different types of human knowledge. Finally, some suggestions are made for the application of the psychological work discussed to the practical problem of knowledge elicitation.     

Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach

Evaluating teaching performance is a main means to improve teaching quality and can plays an important role in strengthening the management of higher education institutions. In this paper, we present a novel framework for teaching performance evaluation based on the combination of fuzzy AHP and fuzzy comprehensive evaluation method. Specifically, after determining the factors and sub-factors, the teaching performance index system was established. In the index system, the factor and sub-factor weights were then estimated by the extent analysis fuzzy AHP method. Employing the fuzzy AHP method in group decision-making can facilitate a consensus of decision-makers and reduce uncertainty. On the basis of the system, the fuzzy comprehensive evaluation method was employed to evaluate teaching performance. A case application was also used to illustrate the proposed framework. The application of this framework can make the evaluation results more scientific, accurate, and objective. It is expected that this work may serve as an assistance tool for managers of higher education institutions in improving the educational quality level.     

Toward Reducing Human Involvement in Validation of Knowledge-Based Systems

Human experts employed in validation exercises for knowledge-based systems (KBSs) often have limited time and availability. Furthermore, they often have different opinions from each other as well as from themselves over time. We address this situation by introducing the use of validation knowledge used in prior validation exercises for the same KBS. We present a validation knowledge base (VKB) that is the collective best experience of several human experts. The VKB is constructed and maintained across various validation exercises, and its primary benefits are given as follows: 1) more reliable validation results by incorporating external knowledge and 2) decrease of the experts' workload. We also present the concept of validation expert software agents (VESAs), which represent a particular expert's knowledge. VESA is a software agent corresponding to a specific human expert. It models the validation knowledge and behavior of its human counterpart by analyzing similarities with the responses of other experts. After a learning period, it can be used to temporarily substitute for its corresponding human expert. We also describe experiments with a small prototype system to evaluate the usefulness of these concepts     

Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragon, Spain, in which it is necessary to extract automatically relevant and interesting information that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts.     

Informational Modeling of the Behavior of a Teacher in the Learning Process Based on Fuzzy Logic

The purpose of this paper is to develop rules, knowledge, and algorithms that will engender a real shift in most of the educational process on the computer environment. On the basis of artificial neural networks, this work is a mechanism that allows taking into account the subjective opinion of experts without requiring changes in the system. With the help of fuzzy logic, this paper offers an original method of monitoring a student's knowledge by closely mimicking the behavior of the teacher in the student survey, which combines the power and brevity, not previously accessible to automated systems.     

A fuzzy inference system for pump failure diagnosis to improve maintenance process: The case of a petrochemical industry

Pump operating problems may be either hydraulic or mechanical and there is interdependence between the failure diagnoses of these two categories. Consequently, a correct diagnosis of a pump failure needs to consider many symptoms and hydraulic or mechanical causes. But, due to nonlinear, time-varying behavior and imprecise measurement information of the systems it is difficult to deal with pumps failures with precise mathematical equations, while human operators with the aid of their practical experience can handle these complex situations, with only a set of imprecise linguistic if-then rules and imprecise system state, but this procedure is time consuming and needs the knowledge of human experts and experienced maintenance personnel. The purpose of this study is to provide a correct and timely diagnosis mechanism of pump failures by knowledge acquisition through a fuzzy rule-based inference system which could approximate human reasoning. The proposed fuzzy inference system by: (1) reduction of human error, (2) reduction of repair time (3) creation of expert knowledge which could be used for training (4) reduction of unnecessary expenditures for upgrades and finally, (5) reduction of maintenance costs, will improve the maintenance process. The novelty of this work is the knowledge acquisition (the extraction of linguistic rules) through the interactive impact of the critical failure modes on the both hydraulic and mechanical operating parameters including flow rate, discharge pressure, NPSHR (Net Positive Suction Head Required), BHP (Brake Horse Power), efficiency, vibration and temperature. The proposed approach is tested and applied to a petrochemical industry.     

Towards a distinctive body of knowledge for Information Systems experts: coding ISD process knowledge in two IS journals

Abstract.  This paper introduces the idea of coding a practically relevant body of knowledge (BoK) in Information Systems (IS) that could have major benefits for the field. In its main part, the paper focuses on the question if and how an underlying body of action-oriented knowledge for IS experts could be distilled from the IS research literature. For this purpose the paper identifies five knowledge areas as the most important parts for an IS expert's BoK. Two of these are claimed as distinct areas of competence for IS experts: IS application knowledge and IS development (ISD) process knowledge. The paper focuses particularly on ISD process knowledge because it allows the organizing of practically relevant IS knowledge in an action-oriented way. The paper presents some evidence for the claim that a considerable body of practically relevant IS process knowledge might, indeed, exist, but also notes that it is highly dispersed in the IS literature. It then argues that the IS research community should take stock of this knowledge and organize it in an action-oriented way. Based on results from prior work it proposes a four-level hierarchical coding scheme for this purpose. In order to test the idea of coding action-oriented knowledge for IS experts, the paper reports the results of a coded literature analysis of ISD research articles published from 1996 to 2000 in two leading IS journals – Information Systems Journal and MIS Quarterly. The results suggest that ISD approaches form a useful framework for organizing practically relevant IS knowledge.     

Group decision-making model with incomplete fuzzy preference relations based on additive consistency.

In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree.     

PERFORMANCE EVALUATION OF SUGAR PLANTS BY FUZZY TECHNIQUE FOR ORDER PERFORMANCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)

This study presents the performance evaluation of sugar plants using the technique for order performance by similarity to ideal solution (TOPSIS) under a fuzzy environment. First, the decision criteria used to evaluate the performances are determined, and then the data from financial statements are collected from sugar plants. Accordingly, the ratings of various alternatives under various criteria and the importance weights of various criteria are assessed by evaluators using linguistic terms. The data obtained are converted into a fuzzy triangular number system and then the fuzzy TOPSIS method is applied to make a final decision. According to the closeness coefficients, the sugar plants are ranked from strong to weak. A real case study involving eight evaluation criteria and nine sugar plants assessed by nine evaluators is provided to illustrate the proposed method. The results show that this method is an effective tool for evaluating investment risks based on the heuristic knowledge acquired from experts.