To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combi...

In this article the issue of data based modeling is dealt with the help a network of uniform multivariate fuzzy classifiers. Within this framework the innovation consists in the specification of a hierarchical design strategy for such a network. Concretely, the two network specifying factors, namely the layout of the network structure and the classifier nodes configuration, will be addressed by...

Land covers mix and high input dimension are two important issues that affect the classification accuracy of remote sensing images. Fuzzy classification has been developed to represent the mixture of land covers. Two fuzzy classifiers of fuzzy rules-based (FRB) and fuzzy neural network (FNN) were studied to illustrate the interpretability of fuzzy classification. Based on the FNN classifier, a ...

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform on-line reinforcement learning by means of Michigan-style fuzzy classifier systems, this issue becomes even more difficult. Indeed, rule generalization (description of state-action relationships ...

One of the major developments in machine learning in the past decade is the Ensemble method, which finds a highly accurate classifier by combining many moderately accurate component classifiers. In this paper, we propose a classifier of integrated neuro-fuzzy system with Adaboost algorithm. It is called Hybrid-neuro-fuzzy system and Adaboost-classifier classifier. Herein, Adaboost creates a col...

Exploiting the information in low quality datasets has been recently acknowledged as a new challenge in Genetic Fuzzy Systems. Owing to this, in this paper we discuss the basic principles that govern the extension of a fuzzy rule based classifier to interval and fuzzy data. We have also applied these principles to the genetic learning of a simple cooperative-competitive algorithm, that becomes ...

In this paper a new linear matrix inequality (LMI) based design method for T-S fuzzy classifier is proposed. The various design factors including structure of fuzzy rule and various parameters should be considered to design T-S fuzzy classifier. To determine these design factors, we describe a new and efficient two-step approach that leads to good results for classification problem. At first, L...

We discuss an approach to change detection in digital remotely sensed imagery that relies on the Fuzzy Post Classification Comparison technique. We use the fuzzy -means classifier together with the Mahalanobis distance as the basis for a metric of class membership for a individual pixel. We note that the value of the fuzzy exponent in a fuzzy classifier is based on the ratios of the reciprocals...

A fuzzy classifier system framework is proposed which employs a tree-based representation for fuzzy rule (classifier) antecedents and genetic programming for fuzzy rule discovery. Such a rule representation is employed because of the expressive power and generality it endows to individual rules. The framework proposes accuracy-based fitness for individual fuzzy classifiers and employs evolution...

Fuzzy approaches are one of the widely used artificial intelligence techniques in the field of ophthalmology. These techniques are used for classifying the abnormal retinal images into different categories that assist in treatment planning. The main characteristic feature that makes the fuzzy techniques highly popular is their accuracy. But, the accuracy of these fuzzy logic techniques depends ...