Summary: This article describes the features of the economic environment. We characterize the external and internal factors impact on the economic projects. The definition of the concept of fuzzy logic and linguistic variable and their relationship with the economy as a whole. The features of a fuzzy model and the possibility of applying this method in the economy. The conclusions about the prospects of the use of fuzzy modeling method in economic planning.
Key words: economy, fuzzy logic, linguistic variable, modeling.
Economic sciences
УДК 339.138
Hassan Ali al-Ababneh
Ph.D., graduate student of International Economics,
National Aviation University
RELEVANCE OF FUZZY LOGIC IN THE ECONOMY
Summary: This article describes the features of the economic environment. We characterize the external and internal factors impact on the economic projects. The definition of the concept of fuzzy logic and linguistic variable and their relationship with the economy as a whole. The features of a fuzzy model and the possibility of applying this method in the economy. The conclusions about the prospects of the use of fuzzy modeling method in economic planning.
Key words: economy, fuzzy logic, linguistic variable, modeling.
Under market conditions, any economic entity in its activity inevitably faces uncertainty. Even high-class specialist is unable to predict the changes that may occur in the external environment. Planning is one of the components of the controlling of business processes, it is the way to reduce uncertainty and risk. However, any, even the largest firm can not afford to completely eliminate uncertainty and, therefore, absolutely take into account the impact of all factors.
When you are working with precise parameters and systems, everything is quite simple. In another case with fuzzy systems. It operates so-called principle of incompatibility: to obtain definite conclusions about the behavior of a complex system should be involved in its analysis of the approaches that utilize the principles of fuzzy logic.
Fuzzy logic - is a branch of mathematics which deals with complex classical logic and the theory of fuzzy sets. The main characteristic of the fuzzy set theory is the manipulation of a component as a linguistic variable. A linguistic variable is a variable whose values are not numbers, and words and expressions, which cause blurred because they do not have a specific numeric value.
Each linguistic variable consists of:
• title;
• the set of its values, which is also called the base-set term;
• universal set X;
• syntactic rules G, which are generated by the new terms by using the words natural or formal language;
• semantic rules P, which each value of the linguistic variable assigns a fuzzy subset of X.
Often the output data for the solution of economic problems are the opinions and conclusions of the experts presented by phrases and words, or linguistic data, so there is a need to transform the linguistic parameters in numeric expressions. That is the problem and solve the theory of fuzzy sets.
Limitations and disadvantages of the use of "classical" formal methods in solving semistructured problems are the result of articulated founder of the theory of fuzzy sets, LA Zadeh's principle of incompatibility: «... the closer we come to the solution of real world problems, it is clear that with increasing complexity of the system our ability to make accurate and confident conclusions about its behavior is reduced to a certain threshold, beyond which precision and confidence are almost mutually exclusive» [5, p. 165].
Disclosure of uncertainty in an unstable environment may be classical probabilistic and statistical methods, but it is an average assessment with a fictitious character. In volatile market situation, the application of statistical methods correctly, and then decisions should be made according to the rules of the principal settings of the decision maker with regard to the phenomenon of uncertainty.
In such situations, decision-making is largely accounted based on expert assessments. However, any expert opinion, even made of the exact objective data, it is much more uncertain than a complex multi-dimensional data set, which is available in a comprehensive manner is extremely difficult (and sometimes impossible). Thus, although expert opinion may contain generalizations and predictions that are relevant to the practice, it does not reduce the level of uncertainty.
It should be noted that another source of uncertainty may be the decision maker. One of the problems associated with it - a fuzziness in concepts, judgments and preferences, the uncertainty of the time interval in which is stored the monotony of human preferences and judgments [1, p. 312].
Methods based on the theory of fuzzy sets, refer to the methods of evaluation and decision-making under uncertainty. Their use involves the formalization of baselines and targets for the efficiency of the process as a vector of interval values (fuzzy interval), hit in each interval is characterized by a certain degree of uncertainty.
By arithmetic and other operations with such fuzzy intervals according to the rules of fuzzy mathematics, experts receive the resulting fuzzy interval for the target. Based on the initial information, experience and intuition of experts often can quite confidently quantify boundaries (intervals) possible (permissible) values of the parameters and the area of their most possible value.
The main disadvantages and limitations of the existing economic and mathematical models and methods to assess the efficacy and the risk of economic projects in the face of uncertainty in comparison with the methods of fuzzy logic are:
To overcome the above drawbacks and limitations of traditional methods justified the use of fuzzy sets theory for the development of models and methods of strategic management of economic activities.
It is worth paying attention to the fact that fuzzy modeling process consists of two key stages:
- identification of the structure (the process of determining the structural characteristics, or the number of fuzzy rules and linguistic terms);
- identification of parameters (selection of antecedent and consequent parameters). It is at this stage minimizes system error, so special attention should be paid to the optimization of this phase in modeling economic problems.
Conclusions. Business Economics is a multifactor system which, moreover, is focused on the end user, to predict the behavioral characteristics of which are quite problematic. In addition, the economy is quite sensitive to the social trends of the industry. Moreover, to predict changes in the economic activity under the influence of external and internal factors in the majority of cases it is possible only in terms linguistic (or fuzzy) concepts. Based on this priority in the economy is the use of fuzzy logic and fuzzy modeling.
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