Some applications of fuzzy logic in rule-based expert systems

1985

Abstract The history and achievements of fuzzy set theory (FST) are considered in relation to developments in expert systems (ESs). An overview is given of early ES developments and the role of linguistic rules and metarules. It is suggested that FST and ESs are not just mathematical and technological advances but also represent major paradigm shifts in system theory. The main shift is away from the normative application of technology to change the world to be theoretically tractable, and towards increasing model realism.

2009 International Conference on Application of Information and Communication Technologies, 2009

This paper is the survey of studies that include design processes of some fuzzy expert systems for applications in some medical area. Recent studies of us that include fuzzy expert systems that make use of fuzzy logic method were described. Some of fuzzy expert systems mentioned in this paper have been developed as first time. Followings were investigated; the risk of prostate cancer, risk of coronary heart disease, degree of child anemia, determination the level of iron deficiency anemia, diagnosis of periodontal dental disease, determination of drug dose and etc. All designed fuzzy expert systems can help in support decision process of physicians. It can be claimed that in many cases such systems can help the physicians in diagnostics, treatment of illness, patient pursuit, prediction of disease risk and etc. As can be seen from the paper accuracies ratios of the proposed FESs were find as high by doctors involved to studies. Robustness and reliabilities of the developed FESs were proved on patients by doctors. The numbers and other arguments that support our claim can be found in the paper.

International Journal of Applied Mathematics, Electronics and Computers, 2014

This paper is the survey of studies that include design processes of some fuzzy expert systems for applications in some medical area. Recent studies of us that include fuzzy expert systems that make use of fuzzy logic method were described. Some of fuzzy expert systems mentioned in this paper have been developed as first time. Followings were investigated; the risk of prostate cancer, risk of coronary heart disease, degree of child anemia, determination the level of iron deficiency anemia, diagnosis of periodontal dental disease, determination of drug dose and etc. All designed fuzzy expert systems can help in support decision process of physicians. It can be claimed that in many cases such systems can help the physicians in diagnostics, treatment of illness, patient pursuit, prediction of disease risk and etc. As can be seen from the paper accuracies ratios of the proposed FESs were find as high by doctors involved to studies. Robustness and reliabilities of the developed FESs were proved on patients by doctors. The numbers and other arguments that support our claim can be found in the paper.

Expert Systems, 1990

Intrinsically vague knowledge is typical of the decision processer involved in medical diagnosis, especially when they are bused e n complex and non numeric data. Lingiiistic descriptions of the symptoms exhibited by a given patient do not, by definition, represent a precise numeric concept and diagnostic classes cannot he modelled in terms of crisp sets. Fuzzy set theory andfuzzy logic provide an appropriate framework for the representation of imprecise medical concepts and imprecise modes of reasoning. A medical expert system with a fuzzy inference model, for the diagnosis of involutional osteoporosis is presented. Medical knowledge is represented in the form of relationships between cornhinations of symptoms and diagnostic classes. Two categories of disease, postmenopausal osteoporosis and senile osteoporosis are identified and three diagnostic classes are introduced for each disease category expressing the degree of severity of the disease: Definite, Possible, Excluded. For each diagnostic class the system infers a conclusion expressing the degree of satisfaction in the choice of the diagnostic class.

1999

This work presents a grid-based fuzzy expert system environment, which employs a repertory grid, fuzzy theory and case-based reasoning. The proposed environment provides friendly user interfaces, abundant data types, intelligent knowledge acquisition facilities and an efficient inference engine. To help users make precise decisions, three factors (i.e., feasibility, reliability and certainty) are used during inference. Moreover, several tools are provided to help experts define membership functions and the knowledge of multimedia data types. Furthermore, the proposed approach is evaluated by implementing an expert system for the diagnosis of animal diseases and by performing several tests.

Bangladesh Journal of …, 2011

We present an application of fuzzy logic for the development of a decision support system in medicine. As a working example we model the prognosis of a patient suffering hearth failure treated with beta-blockers. It is shown how the basic rules, based on expert experience, are represented in a system based on fuzzy logic.

Proceedings of North American Fuzzy Information Processing, 1996

Decision Analysis and Decision Support is an area in which applications of fuzzy set theory can be found since the early 70's. Still there are areas, such as fuzzy controls, that have gained much wider acceptance in practice than fuzzy decision analysis. This paper tries to describe where fuzzy decision support stands now and what would have to be done to gain wider acceptance. One of the major conclusions is, that experimentation is needed and that empirical work in the engineering area is quite different from empirical work in human decision making. Interdisciplinary team work may be much more important in the latter area than in the former.

Computer, 1988

Kong ost of today's commercial expert-system building tools use certainty or confidence factors t o handle uncertainties in the knowledge or data.' But they cannot cope with fuzzy concepts such as tall, good, or hot, which constitute a very significant part of a natural language. In fact, some of these fuzzy concepts have been incorporated into several expert systems, such as Cadiag-2' and Fault,j which are purposely built from a high-level language for a specific domain of application. In Cadiag-2 the knowledge representation of fuzzy concepts is designed specifically for medical diagnosis. In Fault some fuzzy reasoning is also supported. Several AI programming languages, such as FProlog, also provide mechanisms to handle fuzzy concepts.' FProlog is similar t o Prolog except that in FProlog a truth value expressed numerically is allowed in a fact. The uncertainty can then be handled automatically by the FProlog interpreter. This article presents a comprehensive expert-system building tool, called System Z-11, that can deal with exact, fuzzy (or inexact), and combined reasoning, allowing fuzzy and normal terms to be freely mixed in the rules and facts of a n expert system. This fully implemented tool has been used t o build several expert systems in the fields of student curriculum advisement, medical diagnosis, psychoanalysis, and risk analysis. System Z-I1 is a rule-The expert system shell System Z-I1 handles both exact and inexact reasoning. It allows any combination of fuzzy and normal terms and uncertainties. based system that employ furry logic and fuzzy numbers for its inexact reasoning. It uses two basic inexact concepts, fuzziness and uncertainty, which are distinct from each other in the system. Qirtfe .vo~rt7~g, high, and ,eooc/ are f w~! terms. (3) Uncertaintl and tuzziness may occuisim U It aneo U 5 I y i 11 some si t ua t ions :

Advances in Fuzzy Systems, 2013

The theory of fuzzy logic is based on the notion of relative graded membership, as inspired by the processes of human perception and cognition. Lotfi A. Zadeh published his first famous research paper on fuzzy sets in 1965. Fuzzy logic can deal with information arising from computational perception and cognition, that is, uncertain, imprecise, vague, partially true, or without sharp boundaries. Fuzzy logic allows for the inclusion of vague human assessments in computing problems. Also, it provides an effective means for conflict resolution of multiple criteria and better assessment of options. New computing methods based on fuzzy logic can be used in the development of intelligent systems for decision making, identification, pattern recognition, optimization, and control.

This paper deals with Fuzzy Logic and the Real World Applications. Fuzzy logic has rapidly become one of the most successful of today's technologies for developing sophisticated control systems. The reason for which is very simple. Fuzzy logic starts with and builds on a set of user-supplied human language rules. The fuzzy system converts these rules to their mathematical equivalents. This simplifies the job of the system designer and the computer, and results in much more accurate representations of the way systems behave in the real world. It provides both an intuitive method for describing systems in human terms and automates the conversion of those system specifications into effective models.