This paper presents a new kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi–Sugeno–Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A ...

In this paper, we present an algorithm to infer a fuzzy partition over a set of numerical values. This algorithm is based on mathematical morphology and is expressed in theory of formal language. It is usable when the numerical values are associated with a class. We use this algorithm during the construction of a fuzzy decision tree, when no fuzzy partition is available for a numerical attribute.

Overly generalized predictions are a serious problem in concept classi!cation. In particular, the boundaries among classes are not always clearly de!ned. For example, there are usually uncertainties in diagnoses based on data from biochemical laboratory examinations. Such uncertainties make the prediction be more di4cult than noise-free data. To avoid such problems, the idea of fuzzy classi$cat...

Classification rules are an important tool for discovering knowledge from databases. Integrating fuzzy logic algorithms into databases allows us to reduce uncertainty which is connected with data in databases and to increase discovered knowledge’s accuracy. In this paper, we analyze some possible variants of making classification rules from a given fuzzy decision based on cumulative information...

This work focuses on the formalization of a Fuzzy Cognitive Map based decision support system using fuzzy If-Then rules (extracted from data) accompanied with the available experts’ knowledge. The proposed approach is applied to build a Fuzzy Cognitive Map (FCM) grading tool, an advanced FCM-based model used for prediction. The FCM is a modeling methodology based on exploiting knowledge and exp...

The article outlines the results of adaptive fuzzy decision tree development with dynamic structure for automatic process control system of continuous cast billet production. The authors described the structure, mathematical representation as well as adaptation algorithms and structure dynamics.

This paper presents a fuzzy genetic approach to perform data classification. The method aims to obtain small fuzzy classifiers by means of optimization of fuzzy rules bases using a genetic algorithm. It is shown how a fuzzy rules base is generated from a numerical database and how its best subset is found by the genetic algorithm. The classifiers are evaluated in terms of accuracy, cardinality ...

The seminal work of Zadeh (1965), fuzzy set theory (FST) has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability...

A pattern tree [1] is a tree which propagates fuzzy terms using different fuzzy aggregations. Each pattern tree represents a structure for an output class in the sense that how the fuzzy terms aggregate to predict such a class. Unlike decision trees, pattern trees explicitly make use of t-norms (i.e., AND) and t-conorms (OR) to build trees, which is essential for applications requiring rules co...