Introduction: The adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study we used this model in breast cancer detection. Methodology: A set of 1,508 records on cancerous and non-cancerous participant’s risk factors was used.  First,...

Two neural network models, called clustering-RBFNN and clustering-BPNN models, are created for estimating the work zone capacity in a freeway work zone as a function of seventeen different factors through judicious integration of the subtractive clustering approach with the radial basis function (RBF) and the backpropagation (BP) neural network models. The clustering-RBFNN model has the attract...

Subtractive cloning is a powerful technique for isolating genes expressed or present in one cell population but not in another. This method and a related one termed positive selection have their origins in nucleic acid reassociation techniques. We discuss the history of subtractive techniques, and fundamental information about the nucleic acid composition of cells that came out of reassociation...

The design of a neuro-fuzzy system based on a radial basis function (RBF) network architecture and using support vector learning is considered. Typically, a neuro-fuzzy model structure is created from numerical data, however the common modeling techniques may introduce unnecessary redundancy into the rule base. It is of great interest to reduce the number of fuzzy rules. The proposed method pro...

In this paper, a subtractive relational fuzzy c-medoids clustering approach is discussed to identify web user session clusters from weblogs, based on their browsing behavior. In this approach, the internal arrangement of data along with the density of pairwise dissimilarity values is favored over arbitrary starting estimations of medoids as done in the conventional relational fuzzy c-medoids al...

Fuzzy controller’s design depends mainly on the rule base and membership functions over the controller’s input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechan...

Fuzzy theory appears to be extremely effective at handling dynamic, non-linear and noisy data, especially when the underlying physical relationships are not fully understood. Since hydrologists are still uncertain about many of the aspects of the physical processes in the watershed, fuzzy theory has proved to be a very attractive tool enabling them to investigate such problems. The effectivenes...