This paper describes the validity-guided fuzzy clustering evaluation for optimal training of localized neural networks (LNNs) used for reassigning time-frequency representations (TFRs). Our experiments show that the validity-guided fuzzy approach ameliorates the difficulty of choosing correct number of clusters and in conjunction with neural network-based processing technique utilizing a hybrid...

Pattern classification has become an essential element in variety kind of realms such as engineering control and medical diagnosis applications. There are numerous approaches for classification and each of them proved effective in certain cases. Recently, a more general, accurate and efficient method is still desirable and the approaches of fuzzy logic have been successfully applied in this are...

Clustering is one of the technique or approach in content mining and it is used for grouping similar items. Clustering software datasets with mixed values is a major challenge in clustering applications. The previous work deals with unsupervised feature learning techniques such as k-Means and C-Means which cannot be able to process the mixed type of data. There are several drawbacks in the prev...

Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Edge is...

In this paper, we introduce the genetic design of independent input rule-based fuzzy neural networks. The premise part of the rules of the proposed networks is realized by partitioning of the independent input space using hard-c means clustering. The independently partitioned spaces express the fuzzy rules for respective inputs. The consequence part of the rules is represented by polynomial fun...

The quality of results for partitioning clustering algorithms depends on the assumption made on the number of clusters presented in the data set. Applying clustering methods on real data missing values turn out to be an additional challenging problem for clustering algorithms. Fuzzy clustering approaches adapted to incomplete data perform well for a given number of clusters. In this study, we a...

We explore an approach to possibilistic fuzzy c-means 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....

This work presents a new hybrid neuro-fuzzy model for automatic learning of actions taken by agents. The main objective of this new model is to provide an agent with intelligence, making it capable, by interacting with its environment, to acquire and retain knowledge for reasoning (infer an action). This new model, named Reinforcement Learning Hierarchical Neuro-Fuzzy Politree (RL-HNFP), and it...

Skin color segmentation by a block histogram-based neural fuzzy network is proposed in this paper. The Hue-Saturation (HS) color model is used. Color information is represented by a block histogram in an HS space image. Several non-uniform quantization approaches on HS space are proposed to represent histogram information as accurately as possible. The neural fuzzy network used is the self-cons...

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The conseq...