engVolume 11, No. 1, 2016
- Molodtsov D.A.
«Dimension in soft topological space»
The notion of dimension, based on family of coverings, is constructed for soft topological space. Some simplest properties of dimension are presented. Dimension of finite spaces and compact sets are investigated. Examples of calculation of the dimension are considered.Keywords: soft topology, soft dimension of a set.
Resources: first page of the article.
Bibliographic citationMolodtsov D.A. Dimension in soft topological space. Nechetkie Sistemy i Myagkie Vychisleniya [Fuzzy Systems and Soft Computing], 2016, vol. 11, no. 1, pp. 5-18. (in Russian) - Soldatenko I.S.
«On solution method for possibilistic optimization problem of one class with parameters characterized by quasiconcave upper semicontinuous strictly unimodal distribution functions»
The problem of possibilistic level optimization with parameters characterized by quasiconcave upper semicontinuous strictly unimodal distribution functions is studied. The equivalent crisp analogue is constructed for the problem. We use the weakest and the strongest triangular norms in order to aggregate fuzzy information. Results obtained in the article generalize the case when parameters of the task are characterized by parameterized fuzzy numbers of (L,R)-type.Keywords: possibilistic programming, level optimization, triangular norm, weakest t-norm T_W, indirect solution method, equivalent crisp analogue.
Resources: first page of the article.
Bibliographic citationSoldatenko I.S. On solution method for possibilistic optimization problem of one class with parameters characterized by quasiconcave upper semicontinuous strictly unimodal distribution functions. Nechetkie Sistemy i Myagkie Vychisleniya [Fuzzy Systems and Soft Computing], 2016, vol. 11, no. 1, pp. 19-32. (in Russian) - Arefiev V.I., Petrov M.O., Talalaev A.B., Sorokin S.V., Yazenin A.V.
«Classification methods of system condition based on soft computing technology»
Classification methods of system condition are presented. Proposed method are based on soft computing technology. Possibilities of the approach are demonstrated on model examples.Keywords: classification, condition of systems, theory of possibility, fuzzy sets, fuzzy (possibilistic) variable, fuzzy random variable, linguistic variable, soft computing, aggregation function.
Resources: first page of the article.
Bibliographic citationArefiev V.I., Petrov M.O., Talalaev A.B., Sorokin S.V., Yazenin A.V. Classification methods of system condition based on soft computing technology. Nechetkie Sistemy i Myagkie Vychisleniya [Fuzzy Systems and Soft Computing], 2016, vol. 11, no. 1, pp. 33-56. (in Russian) - Filatova N.N., Sidorov K.V.
«Interpretation of the emotion characteristics through the analysis of attractors reconstructed on EEG signals»
The article describes a dynamic model that allows to monitor a change in the direction of human emotions development in the course of a sequence reactions to external stimuli. This problem is solved by forming a new system of signs characterizing the morphology of the attractor reconstructed from the EEG signals. Changes in the testee emotional state are evaluated using a fuzzy estimation of the three characteristics of the attractors reconstructed EEG signals. The emotions monitoring results are presented in the form of a matrix with increments of the attractors characteristics assessments, defined by the additional index scale that allows for each element of a fuzzy set to generate a numerical evaluation using the index term and the membership function.Keywords: model of emotion, emotion recognition, attractor, fuzzy sets.
Resources: first page of the article.
Bibliographic citationFilatova N.N., Sidorov K.V. Interpretation of the emotion characteristics through the analysis of attractors reconstructed on EEG signals. Nechetkie Sistemy i Myagkie Vychisleniya [Fuzzy Systems and Soft Computing], 2016, vol. 11, no. 1, pp. 57-76. (in Russian)