This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by mea...

Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied. For the most part, we lack numerical tools to quantify the mutual information between fuzzy random variables. Therefore, these algorithms (discretization, instance selection, feature selection, etc.) have to use crisp estimations of the interdependency between continuous variables, whose application to...

FCM-type cluster validation is a technique for searching for the optimal fuzzy partition, in which the number of clusters is evaluated by considering the degree of overlapping of fuzzy memberships, cluster compactness or cluster separation. In this paper, a new approach for FCM-type cluster validation in fuzzy co-clustering is proposed. Because fuzzy co-clustering does not use cluster prototype...

Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions ...

Soft Clustering plays a very important rule on clustering real world data where a data item contributes to more than one cluster. Fuzzy logic based algorithms are always suitable for performing soft clustering tasks. Fuzzy C Means (FCM) algorithm is a very popular fuzzy logic based algorithm. In case of fuzzy logic based algorithm, the parameter like exponent for the partition matrix that we ha...

This paper propose an accurate Fuzzy model for describing dynamic hysteresis of ferromagnetic material from measured data using soft computing approaches. We highlighted a fuzzy dynamic model based on measured and normalized input/output data on a C core transformer made of 0.33mm laminations of cold rolled SiFe. Membership’s functions of fuzzy rules are obtained by using the Expectation-Maximi...

In this paper, a new cluster validity criterion for fuzzy c-regression models (FCRM) clustering algorithm with affine linear functional cluster representatives is proposed. The proposed cluster validity criterion calculates the overall compactness and separateness of the FCRM partition and then determines the appropriate number of clusters. Besides, its application to fuzzy model identification...

It is constructively proved that the multi-input–multi-output fuzzy systems based upon genuine many-valued implications are universal approximators (they are called Boolean type fuzzy systems in this paper). The general approach to construct such fuzzy systems is given, that is, through the partition of the output region (by the given accuracy). Two examples are provided to demonstrate the way ...

A new notion of adjoint fuzzy partition is introduced and the reconstruction of a function from its F-transform components is analyzed. An analogy with the Nyquist-Shannon-Kotelnikov sampling theorem is discussed.

Fuzzy time series techniques are more suitable than traditional time series techniques in forecasting problems with linguistic values. Two shortcomings of existing fuzzy time series forecasting techniques are they lack persuasiveness in dealing with recurrent number of fuzzy relationships and assigning weights to elements of fuzzy rules in the defuzzification process. In this paper, a novel fuz...