Use Of Fuzzy Sets Research Articles

Intelligent Real-Time Traffic Control

The task of controlling urban traffic requires flexibility, adaptability, and handling of uncertain information spread through the urban network. The use of fuzzy sets, case-based mechanisms, and genetic algorithms in a distributed system framework can provide the necessary foundations for an efficient and adaptable on-line traffic control system. In this paper we present a distributed traffic control system built upon a fuzzy, adaptive framework. A traffic network consisting of 16 traffic lights in six intersections was implemented and compared with conventional traffic control approaches. Simulation results show that the distributed system outperforms current traffic engineering strategies.

POSSIBILISTIC CAUSAL DIAGNOSIS: APPLICATION TO ENGINE DYNO TEST BENCHES

A causal diagnosis solution relying on experts' knowledge concentrates on the different malfunctions that may disturb the data acquisition process and tells the engine test bench tuning engineer which malfunction has occurred or which malfunctions are most likely suspected. The use of fuzzy sets and possibility theory provides better feedback and knowledge representation. The general architecture of the system is described, and a prototype of the fault-diagnosis part of this sytem is presented. It concerns the implementation of an (off-line) automatic knowledge formalization system and the implementation of the (on-line) possibilistic causal diagnosis process.

A possibilistic approach to diverse-stressor aquatic ecological risk estimation

A possibilistic approach to assess the risk of co-occurring stressors in an aquatic ecosystem based on the use of fuzzy sets is illustrated at the hand of a hypothetical case study. There are two aspects of importance: a fuzzy stressor response relationship where the response may have reference to a lower level end-point, and a rule-based inference model relating the occurrence of low-level stressors to a high-level ecological goal such as sustainability. The stressor-response is expressed as a conditional possibility. The possibility and necessity measures of the disjunctive composition of the stressor-response with the possibility distribution of the stressors yield an estimate of the ecological risk. Such a possibilistic approach may well serve as a screening procedure in multiple stressor resource management when only qualitative risk assessments are needed. WaterSA Vol.27(3) 2001: 293-302

Discrete large eddy simulation

Despite the intense effort expended towards obtaining a model for describing the turbulent flows of fluid, there is no model at hand that can do an adequate job. This leads us to look for a non-traditional approach to turbulence modeling. In this work we conjoin the notion of large eddy simulation with those of fuzzy sets and neural networks to describe a class of turbulent flows. In previous works we had discussed several issues concerning large eddy simulation such as filtering and averaging. Here, we discuss tile use of fuzzy sets to improve the filtering procedure.

On the use and usefulness of fuzzy sets in medical AI

Since its inception fuzzy set theory has been regarded as a formalism suitable to deal with the imprecision intrinsic to many medical problems. Based on a literature survey on the first 30 years, we investigate the impact fuzzy set theory has had on the work in medical AI and point out what it is most appreciated for.

Genetic optimisation of a fuzzy distribution model

Distribution problems deal with distribution from a number of sources to a number of destinations. Each source offers amounts of goods, while each destination demands quantities of these goods. The object is to find the cheapest transporting schedule that satisfies demand without violating supply restraints. In this paper we propose the use of fuzzy sets to represent the provisional information related to costs, demands and other variables. Moreover, we suggest including the problem of shortest route for the distribution vehicles. Finally, to solve this complex problem we propose using a genetic algorithm with a fuzzy fitness function.

An Approximate Reasoning-Based Method for Screening High-Level-Waste Tanks for Flammable Gas

The in situ retention of flammable gas produced by radiolysis and thermal decomposition in high-level waste can pose a safety problem if the gases are released episodically into the dome space of a storage tank. Screening efforts at the Hanford site have been directed at identifying tanks in which this situation could exist. Problems encountered in screening motivated an effort to develop an improved screening methodology. Approximate reasoning (AR) is a formalism designed to emulate the kinds of complex judgments made by subject matter experts. It uses inductive logic structures to build a sequence of forward-chaining inferences about a subject. Approximate-reasoning models incorporate natural language expressions known as linguistic variables to represent evidence. The use of fuzzy sets to represent these variables mathematically makes it practical to evaluate quantitative and qualitative information consistently. In a pilot study to investigate the utility of AR for flammable gas screening, the effort to implement such a model was found to be acceptable, and computational requirements were found to be reasonable. The preliminary results showed that important judgments about the validity of observational data and the predictive power of models could be made. These results give new insights into the problems observed in previous screening efforts.

Examination of the use of fuzzy sets to describe relative speed perception

In recent years a range of new methods have been proposed with which to describe and evaluate driver behaviour. One such method is that of fuzzy logic, where variables used in the driver decision-making process may be described linguistically, allowing a quantifiable degree of uncertainty to be introduced. This paper explores the use of such a formalism to describe the driver perception of ‘closing speed’ between two vehicles engaged in ‘car-following’ on a motorway, and by using data from an instrumented vehicle experiment, it tests a number of models using relative speed, visual angle and the time to collision. Several of these models fit the data quite well, and there is both a small positive perception bias present and a number of reversals in sign judgement. Additionally, a brief examination is made of potential variations on the methodology that may both make data collection easier and/or allow a ‘more fuzzy’ representation to be made.

Designing decision trees with the use of fuzzy granulation

In this study, we discuss the use of fuzzy sets regarded as a well-rounded algorithmic vehicle in the construction of decision trees. The concept of fuzzy granulation realized via context-based clustering is aimed at the quantization (discretization) of continuous attributes as well as handling continuous classes encountered in classification problems. Two detailed experimental studies are presented concerning well-known data sets available on the Web.

The use of fuzzy sets in the evaluation of the environment of coastal waters

This paper advocates the use of GIS for the evaluation of the vulnerability of coastal waters and presents a method for mapping their vulnerability to algal blooms. The method incorporates probability mapping of parameters used to describe coastal waters and fuzzy sets. To allow the modelling of processes where some components cannot substitute for other components the 'no trade off' convex combination formula is proposed. The maps of vulnerability for the Gulf of Gdansk, which were created using this method, may be used to find out which rivers and water discharges play a dominant role in eutrophication.

Neumann expansion for fuzzy finite element analysis

This paper presents a new framework to predict a structure’s effective properties and sensitivities to multiple simultaneous uncertain endogenous parameters. The methodology is based on the use of fuzzy sets and this paper extends the fuzzy set theory to a dynamic finite element analysis of engineering systems containing uncertainty on material properties. A general algorithm, which can resolve the uncertain eigenvalue problem by using a Neumann expansion, is studied. This algorithm is applied to the study of the modal behavior of structures presenting uncertain material properties. Finally, the entropy and the specificity of fuzzy responses lead to the identification of a plate structure’s most sensitive eigenvalue to uncertain sources.

A general approach to solving a wide class of fuzzy optimization problems

Results of research into the use of fuzzy sets for handling various forms of uncertainty in the optimal design and control of complex systems are presented. A general approach to solving a wide class of optimization problems containing fuzzy coefficients in objective functions and constraints is described. It involves a modification of traditional mathematical programming methods and is associated with formulating and solving one and the same problem within the framework of mutually conjugated models. This approach allows one to maximally cut off dominated alternatives from below as well as from above. The subsequent contraction of the decision uncertainty region is associated with reduction of the problem to multicriteria decision making in a fuzzy environment. The general approach is applied within the context of a fuzzy discrete optimization model that is based on a modification of discrete optimization algorithms. Prior to application of these algorithms there is a transition from a model with fuzzy coefficients in objective functions and constraints to an equivalent analog with fuzzy coefficients in objective functions alone. The results of the paper are of a universal character and are already being used to solve problems of power engineering.

On replacement models via a fuzzy set theoretic framework

Uncertainty is present in virtually all replacement decisions due to unknown future events, such as revenue streams, maintenance costs, and inflation. Fuzzy sets provide a mathematical framework for explicitly incorporating imprecision into the decision making model, especially when the system involves human subjectivity. This paper illustrates the use of fuzzy sets and possibility theory to explicitly model uncertainty in replacement decisions via fuzzy variables and numbers. In particular, a fuzzy set approach to economic life of an asset calculation as well as a finite-horizon single asset replacement problem with multiple challengers is discussed. Because the use of triangular fuzzy numbers provides a compromise between computational efficiency and realistic modeling of the uncertainty, this discussion emphasizes fuzzy numbers. The algorithms used to determine the optimal replacement policy incorporate fuzzy arithmetic, dynamic programming (DP) with fuzzy rewards, the vertex method, and various ranking methods for fuzzy numbers. A brief history of replacement analysis, current conventional techniques, the basic concepts of fuzzy sets and possibility theory, and the advantages of the fuzzy generalization are also discussed.

The use of fuzzy sets for resource allocation in an advance request vehicle brokerage system—a case study

The use of fuzzy sets for resource allocation in an advance request vehicle brokerage system—a case study

The Use of Fuzzy Sets for Resource Allocation in an Advance Request Vehicle Brokerage System-A Case Study

The Use of Fuzzy Sets for Resource Allocation in an Advance Request Vehicle Brokerage System-A Case Study

The use of fuzzy sets for resource allocation in an advance request vehicle brokerage system—a case study

Community transport vehicle brokerage operators match the needs of groups without minibuses with the spare capacity of groups which own minibuses. They take bookings in advance, and allocate vehicles without knowing what the total demand will be for travel at a certain time. An approach has been developed which ranks vehicles according to a sequence number which indicates how ‘difficult to book’ they are, and presents the most difficult to book to the broker first, with the intention of maintaining the maximum flexibility in the system. The concept difficult to book is not easy to define exactly. Fuzzy set theory provides a mechanism for dealing with imprecise concepts, and through fuzzy logic for reasoning about such concepts. Fuzzy inferencing systems consist of linguistic variables, fuzzy sets, rules, and methods to combine the sets and produce a final result. Fuzzy logic has been used to provide a means of automating the allocation of sequence numbers to vehicles. Three different approaches to developing the rules for this system have been tested, by simulating bookings, and the one which produced the best results was based on a knowledge acquisition process involving booking staff. This approach resulted in the allocation of the highest number of trips and in leaving the least hours of travel unallocated.

Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty

In classical Constraint Satisfaction Problems (CSPs) knowledge is embedded in a set of hard constraints, each one restricting the possible values of a set of variables. However constraints in real world problems are seldom hard, and CSP's are often idealizations that do not account for the preference among feasible solutions. Moreover some constraints may have priority over others. Lastly, constraints may involve uncertain parameters. This paper advocates the use of fuzzy sets and possibility theory as a realistic approach for the representation of these three aspects. Fuzzy constraints encompass both preference relations among possible instantiations and priorities among constraints. In a Fuzzy Constraint Satisfaction Problem (FCSP), a constraint is satisfied to a degree (rather than satisfied or not satisfied) and the acceptability of a potential solution becomes a gradual notion. Even if the FCSP is partially inconsistent, best instantiations are provided owing to the relaxation of some constraints. Fuzzy constraints are thus flexible. CSP notions of consistency and k-consistency can be extended to this framework and the classical algorithms used in CSP resolution (e.g., tree search and filtering) can be adapted without losing much of their efficiency. Most classical theoretical results remain applicable to FCSPs. In the paper, various types of constraints are modelled in the same framework. The handling of uncertain parameters is carried out in the same setting because possibility theory can account for both preference and uncertainty. The presence of uncertain parameters leads to ill-defined CSPs, where the set of constraints which defines the problem is not precisely known.

Use of fuzzy sets for evaluating shear strength of soils Poon Hwei Chuang, Computers & Geotechnics, 17(4), 1995, pp 425–446.

Use of fuzzy sets for evaluating shear strength of soils Poon Hwei Chuang, Computers & Geotechnics, 17(4), 1995, pp 425–446.

Risk interdependencies and natural language computations

Risk analysis has come to be seen as a quantitative process in which risks are measured by the use of probabilities. However, since every new project is essentially unique with no previous data on it, decisions taken as to the nature of the risks are highly subjective and the actions that may be carried out to mitigate the effects of these risks are not clear‐cut; a non‐numerical approach can, therefore, be more useful. The risk management approach detailed here identifies the risks, checks for dependence amongst risks, and assesses the likelihood of occurrence of each risk by using linguistic variables through the medium of fuzzy sets. The use of linguistic variables is a departure from conventional risk analysis methods that rely rather heavily on statistical analysis to quantify the effects of risks on projects. The entire risk management process is explained, and a case study is carried out to demonstrate the use of the ideas treated. The case study concentrates on the activities of the substructure in a multi‐storey building project. The ten largest risks are identified, and dependence among them is assessed through fuzzy set calculations. The assessment of risk dependencies brings about a reduction in the total number of risks analysed, as highly dependent risks are treated together, and the use of linguistic variables brings about a non‐numeric approach to risk analysis with which project managers can be comfortable. The risk management process, through the use of fuzzy sets, is better able to handle project management knowledge on risk analysis which is highly subjective, and varies from project to project.

An Application of Control System To Cost Variance Analysis

Accounting, particularly in the area of cost variance analysis, contains a great deal of ambiguity due to imprecise or ill‐defined control terms. Cost accountants must continually incorporate good sense and professional judgment in the accounting process to overcome that ambiguity. Because of the construction of accounting expert systems, no ambiguity is present in the facts or rules, thereby excluding human reasoning and analysis of feedback within those systems. The use of fuzzy sets to build fuzzy control systems provides a method to incorporate ambiguity into expert systems, allowing expert systems to more closely emulate the complex human decision making process.