We explore an approach to possibilistic fuzzy clustering that avoids a severe drawback of the conventional approach, namely that the objective function is truly minimized only if all cluster centers are identical. Our approach is based on the idea that this undesired property can be avoided if we introduce a mutual repulsion of the clusters, so that they are forced away from each other. We deve...

This paper proposes the definition of architecture an evolving fuzzy neural network based on self-organizing direction aware data partitioning through stochastic processes dataset used in model. The choice between four different types logical neurons second layer and six neuron activation function that compose artificial aggregation are defined a procedure generating random pairs combinations t...

This paper presents a new fuzzy modeling method that can be classified as a grid partitioning method, in which the domain space is partitioned by the fuzzy equalization method one dimension at a time, followed by the computation of rule weights according to the max-min composition. Five datasets were selected for testing. Among them, three datasets are high-dimensional; for these datasets only ...

membership in each class. This viewpoint not only reflects the reality of many applications in which categories have fuzzy boundaries, but also Provides a simple representstion of the potentially complex partition of the feature space. In brief, we use fuzzy i fthen rules to describe a ChsSifier. A typical fuzzy classification rule is like: Fuzzy classification is the task of partitioning a fea...

this paper presents a comparative study between three versions of adaptive neuro-fuzzy inference system (anfis) algorithms and a pseudo-forward equation (pfe) to characterize the north sea reservoir (f3 block) based on seismic data. according to the statistical studies, four attributes (energy, envelope, spectral decomposition and similarity) are known to be useful as fundamental attributes in ...

In this paper, a fast approach for automatically generating fuzzy rules from sample patterns using generalized dynamic fuzzy neural networks (GD-FNNs) is presented. The GD-FNN is built based on ellipsoidal basis function and functionally is equivalent to a Takagi–Sugeno–Kang fuzzy system. The salient characteristics of the GD-FNN are: 1) structure identification and parameters estimation are pe...