Towards the Future of Fuzzy Logic

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Of these 29 were excluded: four did not meet the language criteria three were written in Chinese and one in Italian and 25 did not meet the inclusion criterion in the evaluation of data stage: the subject of eight papers was not related to nursing, the subject of 22 was not related to fuzzy logic and the authorship or participation of nurses were not identified in 11 papers.

It is important to mention that some papers failed to meet more than one inclusion criterion. One paper not found in the databases was included in the study because it was cited by other two studies and met the inclusion criteria. Hence, 21 were analyzed. A synthesis of the obtained results is presented in Figure 1. The author with the greatest number of publications has four papers related to the subject Im EO. The papers were published over a period of 16 years Nurses were the authors in Fuzzy Logic as Theory.

Six papers discuss theoretical aspects of fuzzy logic based on other studies and on the literature in general. Authors 3 indicate a relationship between fuzzy logic and nursing, suggesting it agrees with the epistemological view of nursing correspondence, coherence and pragmatism and with four important philosophical trends post-empiricism, pragmatism, feminism and postmodernism.

Additionally, nursing phenomena are characterized by complexity, ambiguity, and imprecision, similar to fuzzy logic.

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Considering the use of computer innovations in professional practice, systems based on fuzzy logic apparently obtain better performance than experts in the decision-making process and in describing how this process occurs 7. Hence, fuzzy logic could be employed to help experts to explain how they establish their decisions and even to attribute weight to each of the fuzzy rules used in the process.

Fuzzy logic would help experts to verbalize their decision-making process and this way of understanding could be transmitted in the teaching-learning process from professors to students 7. The application of fuzzy logic in the artificial intelligence field is seen as having great potential for future technologies to be developed in hospital environments 8. Expert nurses are 'expensive products'. Hence, the creation of protocols has been an alternative intended to replace nurse experts. Even though the protocols can be used by technically competent professionals, protocols are not able to provide an individualized and holistic care, which is closely related to the intuition of expert nurses Fuzzy logic reflects the expert nurses' decision-making process.

Through fuzzy logic one can provide evidence that the decision-making process of experts is performed through an intuitive judgment various parameters are considered , that experts do not strictly follow the rules but consider a set of information to perceive the situation as a whole The use of protocols for ventilator weaning, for instance, is a rigid approach and the decision to wean ventilatory devices requires a complexity greater than what protocols present. In such situations, fuzzy logic could be applied Fuzzy logic as method. One of the methods of analysis found in the studies based on fuzzy logic was c-means, which permits grouping data to estimate each cluster center, the distance among data to the center, and the distance between the centers of two clusters Another method was the Fuzzy Delphi Method, which combines the Delphi method with fuzzy logic.

The method was used for collective decision-making processes used by experts to reach consensus where fuzzy logic resolves the issue of a lack of unanimity among experts Fuzzy logic as model. Fuzzy logic can be used in the development of systems with different purposes such as to control infusion 14 ; oximeters ; to aid nurses in decision-making concerning patients' pain ; to measure patient agitation using a digital image processor 19 and heart beat variation, blood systolic pressure and variability of blood pressure 20 ; to support decision-making in the management of ventilation and oxygenation 21 ; and to detect falls The application of fuzzy logic can have different objectives in the development of systems.

It can be used to model the basis of algorithm rules, capturing the knowledge of experts and dealing with uncertainty 14 ; characterize the variables of a system with fuzzy sets ; administer databases with the opinions of experts 17 ; classify responses detected by systems ; detect and remove data that do not reflect a real situation, and follow-up trends of parameter evolution It can also be applied to model concepts and uncertainty expressed in linguistic 23 terms in the development of models because fuzzy logic allows the use of natural language and expressions such as "always", "frequently", "sometimes", "rarely" or "never", thus aiding in the decision-making process of nurses in the face of an intervention 24 or diagnosis The studies present good results in relation to systems based on fuzzy logic when comparing these to previously used traditional methods , or demonstrating their reliability The relationship established between fuzzy logic and nursing is recent.

The first study with nurse authorship dates from However, the most recent study was conducted in Brazil whose main author is a nurse. An important diversity of countries and authors applying fuzzy logic was identified. We can relate this finding to the broad and relevant contribution of fuzzy logic when applied to nursing and other domains in the health field. It shows how fuzzy logic is disseminated in research and how generic its application can be. The predominance of studies in the English language was expected, since fuzzy logic originated in the United States.

However, the number of studies applying fuzzy logic in the scientific development of nursing is still inexpressive considering its potential usefulness. Fuzzy logic was used as a theoretical concept in several discussions concerning subjects such as technological innovation, nursing phenomena, and the relevance of expert nurses. Fuzzy logic has the potential to be applied in the study of philosophical concepts regarding nursing practice.

The methods of data analysis based on fuzzy logic improve proven accepted methods such as the Delphi technique, very frequently used in research. The scarcity of studies is possibly explained by the fact that few nurses are familiar with the use of fuzzy logic in research methods. This finding may be explained by the fact that fuzzy logic originated in the exact sciences where most technological development takes place.

The use of fuzzy logic was predominantly found in studies developing models and computer programs, effectively contributing to the development of hard technologies. The products developed with fuzzy logic are mainly used in units that provide highly complex care. There seems to be a strong application of fuzzy logic related to the decision-making process, a subject frequently discussed in nursing Nonetheless, the fact that few studies indicate that authors continued their investigations draws our attention.

The studies are recent and the interest in fuzzy logic seems to be growing among researchers. There is a need to improve the developed models, testing them in other contexts and with other populations in order to put them in practice and promote professional development, and improve nursing care delivery. Based on the studied papers we conclude that fuzzy logic has been used by nurses mainly in the decision-making process and in the development of models. Fuzzy logic is consistent with the epistemological and philosophical view of nursing, enabling nurses to deal with complex, ambiguous and inaccurate nursing phenomena.

Even though the use of fuzzy logic as a methodological resource is promising, it has been little explored. The use of fuzzy logic in nursing research began only recently. However nurses from different countries and continents have conducted studies using fuzzy logic showing that interest in the subject is universal. Given the preceding discussion, we suggest the development of further research and application of fuzzy logic in theoretical and methodological aspects or in the development of models to contribute to nursing practice.

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Fuzzy Logic and Applications

Email Address. Sign In. Access provided by: anon Sign Out. In that context, he also derives Bayes' theorem from the concept of fuzzy subsethood.

Towards the Future of Fuzzy Logic by Rudolf Seising, Enric Trillas, Janusz Kacprzyk -

Lotfi A. Zadeh argues that fuzzy logic is different in character from probability, and is not a replacement for it. He fuzzified probability to fuzzy probability and also generalized it to possibility theory. More generally, fuzzy logic is one of many different extensions to classical logic intended to deal with issues of uncertainty outside of the scope of classical logic, the inapplicability of probability theory in many domains, and the paradoxes of Dempster-Shafer theory. Computational theorist Leslie Valiant uses the term ecorithms to describe how many less exact systems and techniques like fuzzy logic and "less robust" logic can be applied to learning algorithms.

Valiant essentially redefines machine learning as evolutionary. In general use, ecorithms are algorithms that learn from their more complex environments hence eco- to generalize, approximate and simplify solution logic. Like fuzzy logic, they are methods used to overcome continuous variables or systems too complex to completely enumerate or understand discretely or exactly. Compensatory fuzzy logic CFL is a branch of fuzzy logic with modified rules for conjunction and disjunction.

When the truth value of one component of a conjunction or disjunction is increased or decreased, the other component is decreased or increased to compensate. This increase or decrease in truth value may be offset by the increase or decrease in another component. An offset may be blocked when certain thresholds are met. Proponents [ who? Compensatory Fuzzy Logic consists of four continuous operators: conjunction c ; disjunction d ; fuzzy strict order or ; and negation n.

The conjunction is the geometric mean and its dual as conjunctive and disjunctive operators.

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FML allows modelling a fuzzy logic system in a human-readable and hardware independent way. The designers of fuzzy systems with FML have a unified and high-level methodology for describing interoperable fuzzy systems. From Wikipedia, the free encyclopedia. This article is about the scientific theory of that name. For other uses, see Fuzzy logic disambiguation. Main article: Fuzzy rule. Main article: Defuzzification. Philosophy portal Psychology portal.

Mathematical principles of fuzzy logic. Dordrecht: Kluwer Academic. Stanford Encyclopedia of Philosophy. Bryant University. Retrieved Information and Control. The Bulletin of Symbolic Logic.

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Archived PDF from the original on CRC Press. World Scientific Press. Creighton University. Archived PDF from the original on 30 July Retrieved 16 July Cybernetics and Systems Analysis. In Bharati Vidyapeeth College of Engineering ed. Soft Computing. Allied Publishers. Retrieved 9 November Expert Systems with Applications.

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Introduction to Fuzzy Logic !

International Journal of General Systems. Bibcode : IJGS Archived from the original on Bibcode : SchpJ Probability" PDF. University of South California. Fuzzy Sets and Systems. New York: Basic Books. On the Power of Fuzzy Markup Language.

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