This paper proposes a novel clustering algorithm for the structure learning of fuzzy neural networks. Our clustering algorithm uses the reward and penalty mechanism for the adaptation of the fuzzy neural networks prototypes at every training sample. Compared with the classical clustering algorithms, the new algorithm can on-line partition the input data, pointwise update the clusters, and self-...

Traditional approaches for modeling TSK fuzzy rules are trying to adjust the parameters in models, and not considering the training data distribution. Hence it will result in an improper clustering structure, especially, when outliers exist. In this paper, a clustering algorithm termed as Robust Proper Structure Fuzzy Regression Algorithm (RPSFR) is proposed to define fuzzy subspaces in a fuzzy...

Fuzzy systems are capable to model the inherent uncertainties in real world problems and implement human decision making. In this paper two issues related to fuzzy systems development are addressed and solutions are proposed and implemented. First issue is related to the high dimensional data sets. Such kinds of data sets lead to explode the search space of generated rules and results into dete...

Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determi...

Finding the collapse susceptible portion of a power system is one of the purposes of voltage stability analysis. This part which is a voltage control area is called the voltage weak area. Determining the weak area and adjecent voltage control areas has special importance in the improvement of voltage stability. Designing an on-line corrective control requires the voltage weak area to be determi...

Traditional approaches for modeling TSK fuzzy rules are trying to adjust the parameters in models, and not considering the training data distribution. Hence it will result in an improper clustering structure, especially, when outliers exist. In this paper, a clustering algorithm termed as Robust Proper Structure Fuzzy Regression Algorithm (RPSFR) is proposed to define fuzzy subspaces in a fuzzy...

Principal methods in nonhierarchical and hierarchical fuzzy clustering are overviewed. In particular, the method of fuzzy c-means is focused upon and recent algorithms in fuzzy c-means are described. It is shown that the concept of regularization plays an important role in the fuzzy c-means. Classification functions induced from fuzzy clustering are discussed and variations of the standard fuzz...

Image processing is an important research area in computer vision. clustering is an unsupervised study. clustering can also be used for image segmentation. there exist so many methods for image segmentation. image segmentation plays an important role in image analysis.it is one of the first and the most important tasks in image analysis and computer vision. this proposed system presents a varia...

In recent years, the Fuzzy Relational Database and its queries have gradually become a new research topic. Fuzzy Structured Query Language (FSQL) is used to retrieve the data from fuzzy database because traditional Structured Query Language (SQL) is inefficient to handling uncertain and vague queries. The proposed model provides the facility for naïve users for retrieving relevant results of no...

A quality of centroid-based clustering is highly dependent on initialization. In the article we propose initialization based on the probability of finding objects, which could represent individual clusters. We present results of experiments which compare the quality of clustering obtained by k-means algorithm and by selected methods for fuzzy clustering: FCM (fuzzy c-means), PCA (possibilistic ...