For complex and high-dimensional problems, data-driven identification of classifiers has to deal with structural issues like the selection of the relevant features and effective initial partition of the input domain. Therefore, the identification of fuzzy classifiers is a challenging topic. Decision-tree (DT) generation algorithms are effective in feature selection and extraction of crisp class...

In recent years, fuzzy modelling has become very popular because of its ability to assign meaningful linguistic labels to fuzzy sets in the rule base. However, in order to achieve better performance in fuzzy modelling, parameter identification often needs to be performed. In this paper, we address this optimization problem using memetic algorithms (MAs) for Sugeno and Yasukawa's (SY) qualitativ...

Fuzzy neural networks have several features that make them well suited to a wide range þÿ o l knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the fonn of semanticallymeaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under co...

Fuzzy systems can approximate any continuous nonlinear function to arbitrary accuracy, provided that suitable fuzzy rules are available (Wang 1994). Recent results show that the fusion of some intelligent technologies with fuzzy systems seems to be very effective for nonlinear systems modelling. In Oh, Pedrycz, and Roh (2006), fuzzy neural networks endowed with polynomial neurons were investiga...

Neuro-fuzzy system (NFS) and especially localized NFS are powerful rule-based methods for knowledge extraction, capable of inducing salient knowledge structures from data automatically. Contemporary localized NFSs, however, often demand large features and rules to accurately describe the overall domain data, thus degrading their interpretability and generalization traits. In light of these issu...

Neuro-fuzzy systems have recently gained a lot of interest in research and application. These are approaches that learn fuzzy systems from data. Many of them use rule weights for this task. In this paper we discuss the innuence of rule weights on the interpretability of fuzzy systems. We show how rule weights can be equivalently replaced by modiications in the membership functions of a fuzzy sy...

A new methodology of extraction, optimization, and application of sets of logical rules is described. Neural networks are used for initial rule extraction, local or global minimization procedures for optimization, and Gaussian uncertainties of measurements are assumed during application of logical rules. Algorithms for extraction of logical rules from data with real-valued features require dete...

In the approximate fuzzy reasoning the covering over of fuzzy rule base input and rule premise of a rule determines the importance of that fuzzy rule and the rule output as well. An axiom system has been created, describing the relationship between the fuzzy rule base system, rule input and rule output. By using distance-based operators a novel reasoning method appears by the compositional rule...

The assessment of students’ metacognitive knowledge and skills about reading is critical in determining their ability to read academic texts and do so with comprehension. In this paper, we used induction trees to extract metacognitive knowledge about reading from a reading strategies dataset obtained from a group of 1636 undergraduate college students. Using a C4.5 algorithm, we constructed dec...

In this paper a neuro-fuzzy modeling framework is proposed, which is devoted to discover knowledge from data and represent it in the form of fuzzy rules. The core of the framework is a knowledge extraction procedure that is aimed to identify the structure and the parameters of a fuzzy rule base, through a two-phase learning of a neuro-fuzzy network. In order to obtain reliable and readable know...