Neuro-fuzzy classi cation systems allow to derive fuzzy classi ers by learning from data. The obtained fuzzy rule bases are sometimes hard to interpret, even if the learning method uses constraints to ensure an appropriate fuzzy partitioning of the input domains. This paper describes an approach to build more expressive rules by performing boolean transformations during and after the learning p...

This paper presents a diagnosis framework based on a qualitative model of the process. Starting from a dynamic abstraction procedure under a defined operating mode a fuzzy partitioning of the variables evolution is made, defining for each measured or observable variable a number of qualitative states. Then time Fuzzy intervals representing the moment of state change are defined. The process beh...

Fuzzy co-clustering is a technique that performs simultaneous fuzzy clustering of objects and features. It is known to be suitable for categorizing high-dimensional data, due to its dynamic dimensionality reduction mechanism achieved through simultaneous feature clustering. We introduce a new fuzzy co-clustering algorithm called Heuristic Fuzzy Co-clustering with the Ruspini’s condition (HFCR),...

Two-mode partitioning is a relatively new form of clustering that clusters both rows and columns of a data matrix. In this paper, we consider deterministic twomode partitioning methods in which a criterion similar to k-means is optimized. A variety of optimization methods have been proposed for this type of problem. However, it is still unclear which method should be used, as various methods ma...

OBJECTIVE The aim of this paper is to present a novel fuzzy classification framework for the automatic extraction of fuzzy rules from labeled numerical data, for the development of efficient medical diagnosis systems. METHODS AND MATERIALS The proposed methodology focuses on the accuracy and interpretability of the generated knowledge that is produced by an iterative, flexible and meaningful ...

Whether the design of knowledge base or the modeling of complex systems, when systems are characterized as complex systems with high dimension and a variety of variables and factors, to reduce complexity are necessary. Fuzzy cognitive maps (FCM) are a soft computing method for simulation and analysis of complex system, which combines the fuzzy logic with theories of neural networks. It is flexi...

A simple and effective fuzzy clustering approach is presented for fuzzy modeling from industrial data. In this approach, fuzzy clustering is implemented in two phases: data compression by a self-organizing network, and fuzzy partitioning via fuzzy cmeans clustering associated with a proposed cluster validity measure. The approach is used to extract fuzzy models from data and find out the optima...

Neuro-fuzzy [Jang,1997][Abraham,2005] are hybrid systems that combine the learning capacity of neural nets [Haykin,1999] with the linguistic interpretation of fuzzy inference systems [Ross,2004]. These systems have been evaluated quite intensively in machine learning tasks. This is mainly due to a number of factors: the applicability of learning algorithms developed for neural nets; the possibi...