In this paper we present NEFCLASS, a neuro--fuzzy system for the classification of data. This approach is based on our generic model of a fuzzy perceptron which can be used to derive fuzzy neural networks or neural fuzzy systems for specific domains. The presented model derives fuzzy rules from data to classify patterns into a number of (crisp) classes. NEFCLASS uses a supervised learning algorithm based on fuzzy error backpropagation that is used in other derivations of the fuzzy perceptron. Introduction Combinations of neural networks and fuzzy systems are very popular (for an overview see [4, 6]), but most of the approaches are not easy to compare because they use very different architectures, activation functions, propagation and learning algorithms, etc. In [5] we presented a fuzzy perceptron as a generic model of multilayer fuzzy neural networks. It can be used as a common base for neuro--fuzzy architectures in order to ease the comparision of different approaches. By applying a...

Neuro--fuzzy systems have recently gained a lot of interest in research and application. In this paper we discuss NEFCLASS, a neuro--fuzzy approach for data analysis. We present new learning strategies to derive fuzzy classification rules from data, and show some results. 1 Introduction In [10] we have presented the NEFCLASS model, a neuro--fuzzy model for data analysis. In this paper we present refined rule learning algorithms for our model. NEFCLASS is used to derive fuzzy rules from a set of data that can be separated in different crisp classes. The fuzzy rules describing the data are of the form: R : if x 1 is 1 and x 2 is 2 and : : : and xn is n then the pattern (x 1 ; x 2 ; : : : ; xn ) belongs to class i, where 1 ; : : : ; n are fuzzy sets. The task of the NEFCLASS model (NEuro Fuzzy CLASSification) is to discover these rules and to learn the shape of the membership functions to determine the correct class or category of a given input pattern. The patterns are vectors x =...

This paper presents a fuzzy perceptron as a generic model of multilayer fuzzy neural networks, or neural fuzzy systems, respectively. This model is suggested to ease the comparision of different neuro--fuzzy approaches that are known from the literature. A fuzzy perceptron is not a fuzzification of a common neural network architecture, and it is not our intention to enhance neural learning algorithms by fuzzy methods. The idea of the fuzzy perceptron is to provide an architecture that can be initialized with prior knowledge, and that can be trained using neural learning methods. The training is carried out in such a way that the learning result is interpretable in the form of linguistic fuzzy if--then rules. Next to the advantage of having a generic model to compare neuro--fuzzy models, the fuzzy perceptron can be specialized e.g. for data analysis and control tasks. 1 Introduction Combinations of neural networks and fuzzy systems have become very popular during the last two years [Be...

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We selected five trainable Neuro-Fuzzy classification algorithms in order to investigate their ability to differentiate areas of malign tissue in ultrasonic prostate images. The algorithms were compared with results from two commonly used classifiers, the K-nearest neighbor (KNN) classifier and the Bayes classifier. The best Neuro-Fuzzy classification system, which is based on a mountain clustering algorithm published by Yager et al and refined by Chiu reached recognition rates above 86 % in comparison to the Bayes classifier (79 %) and the KNN classifier (78 %). Our results suggest that Neuro-Fuzzy classification algorithms have the potential to significantly improve common classification methods for the use in ultrasonic tissue characterization. INTRODUCTION The aim of our work was to investigate the performance of different Neuro-Fuzzy classification methods for the distinction of benign and malign tissue in ultrasound prostate diagnosis. The motivation to use Neuro-Fuzzy systems ...

The interest in neuro--fuzzy systems has grown tremendously over the last few years. First approaches concentrated mainly on neuro--fuzzy controllers, whereas newer approaches can also be found in the domain of data analysis. After successful applications in Japan neuro--fuzzy concepts also find their way into the European industries, though mainly simple models, like FAMs, still prevail. This paper shortly reviews some modern neuro--fuzzy concepts. After this a generic neuro--fuzzy model is presented, that serves a foundation for specific derived neuro--fuzzy applications, this is shown with a model for neuro--fuzzy data analysis, which we see as an important perspective for the neuro--fuzzy domain. The paper concludes with some thoughts on further research directions that go beyond simple neuro--fuzzy control applications. 1 Introduction Neuro--fuzzy systems enjoyed an ever growing popularity over the recent years. The first approaches were mainly neuro--fuzzy controllers, but newer ...