A fuzzy neural networks are connectionist systems that facilitate learning from data, reasoning over fuzzy rules, rule insertion, rule extraction, and rule adaptation. The concept of a particular class of fuzzy neural networks, called FuNNs, is further developed in this paper to a new concept of evolving neuro-fuzzy systems (EFuNNs), with respective algorithms for learning, aggregation, rule in...

this paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. in this method, mammograms are segmented into regions of interest (roi) in order to extract features including geometrical and contourlet coefficients. the extracted features benefit from...

This paper describes a technique that can be used to generate fuzzy rules to extract the features of handwritten characters. The feature extraction is a complicated problem as different people write the same character in different ways. The development of a technique that can generate the description of handwritten characters is still a challenge for the handwritten recognition systems. The fuz...

Data projection is an important tool in exploratory data analysis. Sammon’s non linear projection method lacks predictability and is ineffective for large data sets. To introduce predictability we implement an extension of Sammon’s algorithm using fuzzy logic approach. The fuzzy based rule model is implemented in the .Net framework using Microsoft Visual Studio with Visual C# as the programming...

Rough fuzzy sets are an effective mathematical analysis tool to deal with vagueness and uncertainty in the area of machine learning and decision analysis. Fuzzy information systems and fuzzy objective information systems exit in many applications and knowledge reduction in them can’t be implemented by reduction methods in Pawlak information systems. Therefore, this paper provides a model for ru...

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a. self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usab...

This paper is concerned with the application of a treebased regression model to extract fuzzy rules from highdimensional data. We introduce a locally weighted scheme to the identification of Takagi-Sugeno type rules. It is proposed to apply the sequential least-squares method to estimate the linear model. A hierarchical clustering takes place in the product space of systems inputs and outputs a...

This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from...