Non-stationarity is inherent in EEG data. We propose a concept for an adaptive brain computer interface (BCI) that adapts a classifier to the changes in EEG data. It combines labeled and unlabeled data acquired during normal operation of the system. The classifier is based on Fuzzy Neural Gas (FNG), a prototype-based classifier. Based on four data sets we show that retraining the classifier sig...

This paper investigates the application of the computational model of Cellular Automata (CA) for pattern classification of real valued data. A special class of CA referred to as Fuzzy CA (FCA) is employed to design the pattern classifier. It is a natural extension of conventional CA, which operates on binary string employing boolean logic as next state function of a cell. By contrast, FCA emplo...

This paper introduces a genetic algorithm able to combine different classifiers based on different distance functions. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of a vote strategy. The method has been applied to the classification of four types of biological cells, results show an improvement of the recognition rate using the...

In this work we propose to use fuzzy prototypes for classification tasks. We create the prototypes by first clusterizing, separately, the available data for each class. Then we create a fuzzy set around each class center, according to clusterization indices that take into account both local and global data. We also investigate the use of an index to guide the process of obtaining a suitable num...

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good perfor...

The paper combines two major neuro-fuzzy applications in power engineering: stabilizing power systems at a generation stage and reducing disturbances at a delivery stage. It presents a neural-fuzzy classifier for recognition of power disturbances and a fuzzy excitation controller comprising both the exciter and the power system stabilizer.

In this paper fuzzy hypersphere neural network (FHSNN) is proposed with its learning algorithm, which is used for rotation invariant handwaritten character recognition. The FHSNN utilizes fuzzy sets as pattern classes in which each fuzzy set is an union of fuzzy set hyperspheres. The fuzzy set hypersphere is an ndimensional hypersphere defined by a center point and radius with its membership fu...

The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable versio...

Fuzzy memberships can be understood as coverage functions of random sets. This interpretation makes sense in the context of fuzzy rule learning: a random sets-based semantic of the linguistic labels is compatible with the use of fuzzy statistics for obtaining knowledge bases from data. In particular, in this paper we formulate the learning of a fuzzy rule based classifier as a problem of statis...

Data-based identification of fuzzy rule-based classifiers for high-dimensional problems is addressed. A crisp binary decision tree approach is proposed for the selection of the relevant features and effective initial partitioning of the input domain. The decision tree is then transformed into a fuzzy rule-based classifier. Fuzzy classifiers have more flexible decision boundaries than decision t...