This paper examines several clustering methods for the structure learning in constructing efficient neuro-fuzzy systems. The structure learning establishes the internal structure (i.e., the number of term sets and fuzzyrule base generation) of a given neuro-fuzzy architecture. The fundamental ideas of existing rule generation algorithms are addressed and discussed. Performance of the neuro-fuzz...

This paper shows how a small number of fuzzy rules can be selected for designing interpretable fuzzy rule-based classification systems. Our approach consists of two phases: candidate rule generation by data mining criteria and rule selection by genetic algorithms. First a large number of candidate rules are generated and prescreened using two rule evaluation criteria in data mining. Next a smal...

The definition of the Fuzzy Rule Base is one of the most important and difficult tasks when designing Fuzzy Systems. This paper discusses the results of two different hybrid methods, previously investigated, for the automatic generation of fuzzy rules from numerical data. One of the methods, named DoC-based, proposes the creation of Fuzzy Rule Bases using genetic algorithms in association with ...

The paper presents new strategies for testing and rating the relevance of rules in the Fuzzy{ROSA (Rule Oriented Statistic Analysis) method for data based rule generation. Speciic characteristics and diierences between the proposed strategies are pointed out.

The paper presents new strategies for testing and rating the relevance of rules in the Fuzzy{ROSA (Rule Oriented Statistic Analysis) method for data based rule generation. Speciic characteristics and diierences between the proposed strategies are pointed out.

This project of dissertation presents a method to generate fuzzy rules based on the conditional fuzzy clustering algorithm, that aims at handling the issue of interpretability of the rule base. The balance between interpretability and accuracy of fuzzy rules is addressed by means of the definition of contexts formed with a small number of attributes and the generation of clusters conditioned by...

Inductive learning approaches traditionally categorized as supervised, which use labeled data sets, and unsupervised, which use unlabeled data sets in learning tasks. The great volume of available data and the cost involved in manual labeling has motivated the investigation of different solutions for machine learning tasks related to unlabeled data. The approach proposed here fits into this con...

In this paper a new method of rule generation for hierarchical fuzzy systems (Hierarchical Fuzzy Associative Memory, HIFAM) is described. A HIFAM is structured as a binary tree and overcomes the exponential growth of the rulebases when the number of inputs increases. The training algorithm for HIFAM is suited for approximation and classification problems. Several benchmarks demonstrate that the...

In this paper we present an approach for generation and initialization of fuzzy neural networks (FNN) from data. Fuzzy neural networks are concept that integrates some features of the fuzzy logic and the artificial neural networks theory. Based on analysis of several different fuzzy neural networks models, uniform representation method is presented, and two basic types are identified: FNN based...

In this paper we present a simple belief updating system using recurrent fuzzy rules which improves class prediction in ordered datasets. The recurrent fuzzy rule builds up belief in a class for each point in a sample-ordered or timeordered dataset. Belief in each class is represented by a fuzzy set predicted class defined on the class universe. Belief in a class increases as positive cases are...