Kondratenko N.R.

Improving Adequacy of Type-2 Fuzzy Models by Using Type-2 Fuzzy Sets

An information approach to fuzzy modeling was considered. The present paper formulates the task of developing a formal approach, which would enable analyzing fuzzy systems in terms of their capability to describe uncertainties of input information using interval membership functions. The discussed approach would allow to introduce the information factor for evaluating the quality of fuzzy models functioning using interval membership functions, and to increase the adequacy of the application area representation by a developed fuzzy model.

Interval Fuzzy Clustering Based on Alternative Validity Indices

The paper studies several clustering validity indices (Kwon index, Xie Beni index, partition index) in view of the fuzzy parameter. We reveal the pattern of change in indices being researched against the fuzzy parameter change. We introduce an interval type-2 fuzzy clustering method based on combination of three validity indices. The membership values are presented as intervals. It allows preserving completeness of information on a set of their possible values, as well as reducing the influence of each specific index on uncertainty reflected in the result.