Page 66 - Azerbaijan State University of Economics

THE JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.72, # 2, 2015, pp. 54-72

The proposed technique for a complex evaluation of an innovative potential,
constructed with the use of the theory of fuzzy sets, was not previously applied to
evaluation of an innovative potential for a factorial analysis of the social and
economic environment of the scientific-technological complex of the economic zones.
Implementation of the given method envisages several stages:
-Parametrical values from the corresponding groups of factors are calculated;
- Fuzzification is done – transformation of the design indicators into the values
of linguistic variables with the use of the membership functions. For this purpose
definitions of the linguistic variables and fuzzy subsets for each element are entered.
Belonging of each accurate value to one of the terms of a linguistic variable is
determined by means of a membership function.
Also possible is the use of the arbitrary and standard membership functions;
- At the stage of development of the fuzzy rules, the productional rules,
connecting two linguistic variables, are defined. A set of such rules describes the
management strategy applied for evaluation of an innovative potential;
- At the defuzzification stage generalization is done of the data concerning the
level of an innovative potential into an integrated indicator with account of the
weighting coefficients of the influencing factors.
For evaluation of the level of an innovative potential two linguistic variables
are set. The first variable with the corresponding terms-subsets is introduced for
evaluation of each concrete model element. Evaluation of each indicator is done
according to the standard 3-level scale, where linguistic descriptions: low, medium
and high correspond to the set intervals of the values of indicators (Table 5).
Table 5.Evaluation of the value levels of indicators Gi
Linguistic variables Term (term - subset)
Low (IC) Fuzzy subset of indicator (Gi) for the “low” level
Medium (IC) Fuzzy subset of indicator (Gi) for the “medium” level
High (IC) Fuzzy subset of indicator (Gi) for the “high” level

The above indicators have diverse character, but, since the value of any
quantity indicator is within the interval from 0 up to 1, all the quantitative
evaluations are bound with a linguistic variable. At that, the zero value of a fuzzy
criterion is estimated as the worst of the possible values, and unity as the best. The
second variable with a corresponding term-set is appropriated on the basis of the
data evaluation of each indicator (G) corresponding to the levels of an innovative
66