A fuzzy inverse relation based on godelian logic and its applications

Fuzzy logic applicable to decision-making processes such as diagnostic expert systems and fuzzy controllers is the prime concern of this paper. In these applications, an inverse operation (or backward inference) plays an important role. In the current paper, a new backward inference technique based on Gödelian logic is proposed. The relation between the forward and backward inferences is established by introducing fuzzy joint and conditional relations.     

Evidential support logic programming

An expert system is a computer program which can act in a similar way to a human expert in a restricted domain of application from the point of view of solving problems, taking decisions, planning and giving advice. It consists of two parts. One part is a knowledge base consisting of that knowledge used by the expert in his performance. A second part is an inference engine which allows queries to be answered by asking questions of the environment and performing evidential reasoning.This paper is concerned with the knowledge representation and inference mechanism for evidential reasoning. Man's knowledge consists of statements which cannot be guaranteed to be true and is expressed in a language containing imprecise terms. Uncertainties, either of a probabilistic or fuzzy nature, cannot be ignored when modelling human expertise. Not all practical reasoning takes the form of deductive inference. For practical affairs we use inductive, abductive, analogical and plausible reasoning methods and for each of these the concept of the strength of evidence would seem to be important.We describe a support logic programming system which generalises logic programming to the case in which various forms of uncertainty can be included. In this system a conclusion does not logically follow from some axioms but is supported to a certain degree by means of evidence. The negation of the conclusion is also supported to a certain degree and the two supports do not necessarily add up to one.A calculus for such a support logic programming system is described and applications to its use in expert systems and its use in providing recursive definitions of fuzzy concepts are given.     

Reasoning under uncertainty with FIR methodology

The aim of this research is to develop a new methodology called UNFIR (uncertainty in FIR) as an extension of the fuzzy inductive reasoning (FIR) technique. The main idea behind UNFIR is to expand the modeling capacity of the FIR methodology allowing it to work with classical fuzzy rules. On the one hand, UNFIR is able to automatically construct fuzzy rules starting from a set of pattern rules obtained by FIR. On the other hand, UNFIR affords the prediction of systems behavior by using a mixed pattern/fuzzy inference system that takes advantage of the uncertainty inherent to the data. The pattern rule base that the FIR methodology generates can be very large, obstructing the prediction process and reducing its efficiency. The new methodology preserves as much as possible the knowledge of the pattern rules in a compact fuzzy rule base. In this process some precision is lost but the robustness is considerably increased.The performance of UNFIR methodology as a systems’ prediction tool is also studied in this work. Three different applications are used for this purpose, i.e., a linear system, a non-linear system and an industrial process.     

Systems framework for fuzzy sets in civil engineering

Civil engineering projects and designs are commonly developed in a systems framework that includes different types of uncertainty. In general, uncertainty can be of the ambiguity or vagueness type. The theory of probability and statistics has been extensively used in civil engineering to deal with the ambiguity type of uncertainty. The theory of fuzzy sets and systems have been used in civil engineering to model the vagueness type of uncertainty in many civil engineering applications. In this paper, the role of fuzzy sets in civil engineering systems is described using several example applications, e.g., quality assessment of wildlife habitat, construction engineering and management, structural reliability, and damage assessment of existing structures.     

A structural optimization approach for passanger car tires with consideration of uncertainties

In order to support the design process of passenger car tires, a multi-objective optimization approach based on Finite Element Analysis (FEA) can be utilized. Providing a reliable and high quality design requires the consideration of data uncertainty within the optimization process. This uncertainty is caused predominantly by instable production conditions of tire components as well as incomplete information concerning e.g. loading. In order to capture the state of information which is indeed available in such engineering applications, an uncertainty model, which enables modelling fragmentary or dubious information as well as mathematical formulation of expert specifications and evaluations has to be applied. The presented requirements are fulfilled by the uncertainty model fuzziness. Therefore, an approach of multi-objective optimization with consideration of fuzzy quantities was developed. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

面向复杂产品系统项目评价专家遴选方法研究

专家遴选是复杂产品系统(CoPS)项目科学评价过程中的重要决策问题,科学合理的遴选专家有利于CoPS项目的实现。CoPS项目的多领域知识集成性和项目任务的模糊不确定性,给项目评价的知识属性识别和专家遴选相对“小”领域的科学判断带来较大困难。在面向CoPS项目评价的专家遴选研究问题上,提出了包含技术领域和任务领域的二元知识分类表征方法,构建了基于直觉模糊相似度的专家知识关联度侧度模型,在此基础上建立了包含专家业绩评价和知识关联度的专家遴选多目标决策模型,并给出了一个航天领域电子元器件项目评价的实例。     

Solving Large-Scale Fuzzy and Possibilistic Optimization Problems

Fuzzy and possibilistic optimization methods are demonstrated to be effective tools in solving large-scale problems. In particular, an optimization problem in radiation therapy with various orders of complexity from 1000 to 62,250 constraints for fuzzy and possibilistic linear and nonlinear programming implementations possessing (1) fuzzy or soft inequalities, (2) fuzzy right-hand side values, and (3) possibilistic right-hand side is used to demonstrate that fuzzy and possibilistic optimization methods are tractable and useful. We focus on the uncertainty in the right side of constraints which arises, in the context of the radiation therapy problem, from the fact that minimal and maximal radiation tolerances are ranges of values, with preferences within the range whose values are based on research results, empirical findings, and expert knowledge, rather than fixed real numbers. The results indicate that fuzzy/possibilistic optimization is a natural and effective way to model various types of optimization under uncertainty problems and that large fuzzy and possibilistic optimization problems can be solved efficiently.     

模糊集的相对扰动

在模糊推理的理论研究中,模糊推理方法的扰动性分析是一个重要的研究方向,即讨论模糊推理前件的变化对后件的影响。本文给出模糊集的相对扰动的概念,讨论由模糊集运算产生的相对扰动,以及模糊推理的相对扰动性,给出一些有趣的性质。     

On Providing Quality of Service in Grid Computing through Multi-objective Swarm-Based Knowledge Acquisition in Fuzzy Schedulers

Nowadays, Grid computing is increasingly showing a service-oriented tendency and as a result, providing quality of service (QoS) has raised as a relevant issue in such highly dynamic and non-dedicated systems. In this sense, the role of scheduling strategies is critical and new proposals able to deal with the inherent uncertainty of the grid state are needed in a way that QoS can be offered. Fuzzy rule-based schedulers are emerging scheduling schemas in Grid computing based on the efficient management of grid resources imprecise state and expert knowledge application to achieve an efficient workload distribution. Given the diverse and usually conflicting nature of the scheduling optimization objectives in grids considering both users and administrators requirements, these strategies can benefit from multi-objective strategies in their knowledge acquisition process greatly. This work suggests the QoS provision in the grid scheduling level with fuzzy rule-based schedulers through multi-objective knowledge acquisition considering multiple optimization criteria. With this aim, a novel learning strategy for the evolution of fuzzy rules based on swarm intelligence, Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) is adapted to the multi-objective evolution of an expert grid meta-scheduler founded on Pareto general optimization theory and its performance with respect to a well-known genetic strategy is analyzed. In addition, the fuzzy scheduler with multi-objective learning results are compared to those of classical scheduling strategies in Grid computing.     

Convergence properties and practical estimation of the probability of rank reversal in pairwise comparisons for multi-criteria decision making problems

In this paper, we address the impact of uncertainty introduced when the experts complete pairwise comparison matrices, in the context of multi-criteria decision making. We first discuss how uncertainty can be quantified and modeled and then show how the probability of rank reversal scales with the number of experts. We consider the impact of various aspects which may affect the estimation of probability of rank reversal in the context of pairwise comparisons, such as the uncertainty level, alternative preference scales and different weight estimation methods. We also consider the case where the comparisons are carried out in a fuzzy manner. It is shown that in most circumstances, augmenting the size of the expert group beyond 15 produces a small change in the probability of rank reversal. We next address the issue of how this probability can be estimated in practice, from information gathered simply from the comparison matrices of a single expert group. We propose and validate a scheme which yields an estimate for the probability of rank reversal and test the applicability of this scheme under various conditions. The framework discussed in the paper can allow decision makers to correctly choose the number of experts participating in a pairwise comparison and obtain an estimate of the credibility of the outcome.     

Possibilistic linear regression analysis for fuzzy data

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming problems, the merit of our formulations is to be able to obtain easily fuzzy parameters in possibilistic linear models and to add other constraint conditions which might be obtained from expert knowledge of fuzzy parameters. This approach can be regarded as a fuzzy interval analysis in a fuzzy environment.     

基于犹豫模糊集的CMMI过程评估方法研究

CMMI过程评估是一个典型的多属性决策问题,评估环节采用主任评估师负责制,已有的评估方法得到的结果受到企业的争议.论文从CMMI评估中多个专家参与,专家意见不一致等固有特征出发,在评估中引入犹豫模糊集的思想,将专家打分转换为犹豫模糊值进行计算.基于指标提取、专家打分、数据处理、数据补齐、数据集结等几个环节设计了CMMI过程评估,通过案例计算,得到了客观科学的评估结果.将犹豫模糊集引入到CMMI过程评估中,一方面为CMMI过程评估提供了新的思路和方法,另一方面也拓展了犹豫模糊集的应用领域.     

考虑不确定因素影响的沉淀池优化设计

沉淀池是污水处理流程中一个必不可少的重要环节,设计中存在着大量的不确定因素.本文在沉淀池设计中引入了不确定性模糊非线性规划模型.该模型利用处理效率与沉淀池尺寸之间的非线性关系,求解过程引入区间数和模糊算子,尝试在沉淀池设计中考虑不确定性因素,并取得满意的结果.     

Looking for a good fuzzy system interpretability index: An experimental approach

Interpretability is acknowledged as the main advantage of fuzzy systems and it should be given a main role in fuzzy modeling. Classical systems are viewed as black boxes because mathematical formulas set the mapping between inputs and outputs. On the contrary, fuzzy systems (if they are built regarding some constraints) can be seen as gray boxes in the sense that every element of the whole system can be checked and understood by a human being. Interpretability is essential for those applications with high human interaction, for instance decision support systems in fields like medicine, economics, etc. Since interpretability is not guaranteed by definition, a huge effort has been done to find out the basic constraints to be superimposed during the fuzzy modeling process. People talk a lot about interpretability but the real meaning is not clear. Understanding of fuzzy systems is a subjective task which strongly depends on the background (experience, preferences, and knowledge) of the person who makes the assessment. As a consequence, although there have been a few attempts to define interpretability indices, there is still not a universal index widely accepted. As part of this work, with the aim of evaluating the most used indices, an experimental analysis (in the form of a web poll) was carried out yielding some useful clues to keep in mind regarding interpretability assessment. Results extracted from the poll show the inherent subjectivity of the measure because we collected a huge diversity of answers completely different at first glance. However, it was possible to find out some interesting user profiles after comparing carefully all the answers. It can be concluded that defining a numerical index is not enough to get a widely accepted index. Moreover, it is necessary to define a fuzzy index easily adaptable to the context of each problem as well as to the user quality criteria.     

Applications of the theory of fuzzy sets

This survey summarizes the current state of the theory of fuzzy sets. The use of the theory in applications is illustrated with examples of control and modelling problems, as well as an application to the design of expert systems. Translated from Itogi Nauki i Tekhniki, Seriya Teoriya Veroyatnostei, Matematicheskaya Statistika, Teoreticheskaya Kibernetika, Vol. 29, pp. 83–151, 1990.     

Environmental risk modelling under probability-normal interval-valued fuzzy number

In almost all the realistic circumstances, such as health risk assessment and uncertainty analysis of atmospheric dispersion, it is very essential to include all the information into modelling. The parameters associated to a particular model may include different kind of variability, imprecision and uncertainty. More often, it is seen that available informations are interpreted in probabilistic sense. Probability theory is a well-established theory to measure such kind of variability. However, not all of available information, data or model parameters affected by variability, imprecision and uncertainty can be handled by traditional probability theory. Uncertainty or imprecision may occur due to incomplete information or data, measurement errors or data obtained from expert judgement or subjective interpretation of available data or information. Thus, model parameters, data may be affected by subjective uncertainty. Traditional probability theory is inappropriate to represent them. Possibility theory and fuzzy set theory is another branch of mathematics which is used as a tool to describe the parameters with insufficient or vague knowledge. In this paper, an attempt has been made to combine probability knowledge and possibility knowledge and draw the uncertainty. The paper describes an algorithm for combining probability distribution and interval-valued fuzzy number and applied to environmental risk modelling with a case study. The primary aim of this paper is to propagate the proposed method. Computer codes are prepared for the proposed method using MATLAB.     

Looking for a good fuzzy system interpretability index: An experimental approach

Interpretability is acknowledged as the main advantage of fuzzy systems and it should be given a main role in fuzzy modeling. Classical systems are viewed as black boxes because mathematical formulas set the mapping between inputs and outputs. On the contrary, fuzzy systems (if they are built regarding some constraints) can be seen as gray boxes in the sense that every element of the whole system can be checked and understood by a human being. Interpretability is essential for those applications with high human interaction, for instance decision support systems in fields like medicine, economics, etc. Since interpretability is not guaranteed by definition, a huge effort has been done to find out the basic constraints to be superimposed during the fuzzy modeling process. People talk a lot about interpretability but the real meaning is not clear. Understanding of fuzzy systems is a subjective task which strongly depends on the background (experience, preferences, and knowledge) of the person who makes the assessment. As a consequence, although there have been a few attempts to define interpretability indices, there is still not a universal index widely accepted. As part of this work, with the aim of evaluating the most used indices, an experimental analysis (in the form of a web poll) was carried out yielding some useful clues to keep in mind regarding interpretability assessment. Results extracted from the poll show the inherent subjectivity of the measure because we collected a huge diversity of answers completely different at first glance. However, it was possible to find out some interesting user profiles after comparing carefully all the answers. It can be concluded that defining a numerical index is not enough to get a widely accepted index. Moreover, it is necessary to define a fuzzy index easily adaptable to the context of each problem as well as to the user quality criteria.     

Expected values from probabilities of fuzzy subsets

In many applications in decision theory and expert systems we may have information regarding the probability of some variable being equal to a fuzzy subset. In this paper we provide a methodology for using this information to obtain the expected value of the variable.     

Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems

This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.     

Current research directions in the development of expert systems based on belief networks

An expert system is a computer program that is designed to solve problems at a level comparable to that of a human expert in a given domain. Often expert systems require a representation of uncertainty. This paper highlights some of the key developments in the history of representing uncertainty in expert systems. An uncertainty representation called belief networks is then introduced and its use in expert systems is motivated. The paper concludes with a discussion of current directions in belief network research.