The prevalent communications networks suffer from lack of spectrum and spectrum inefficiency. This has motivated researchers to develop cognitive radio (CR) as a smart and dynamic radio access promised solution. A major challenge to this new technology is how to make fair assignment of available spectrum to unlicensed users, particularly for smart grids communication. This paper introduces an i...

Abstract: In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central-limit theorem, is also shown. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence betw...

This document describes the architecture of neuro fuzzy systems. First part of the document provides a review of general notions of fuzzy logic, and structure of fuzzy systems. A procedure and different types of fuzzy reasoning are described. The second part is devoted to the neural networks and to the simplest model of artificial neuron. In the third part, the similarities and differences betw...

In this paper we present NEFCLASS, a neuro{fuzzy system for the classiication 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 spe-ciic domains. The presented model derives fuzzy rules from data to classify patterns into a number of (crisp) classes. NEFCLASS uses a supervised learning algorit...

Hyperknowledge is formed as a system of sets of interlinked concepts, much in the same way as hypertext is built with interlinked text strings; then hyperknowledgefunctions would be constructs which link concepts / systems of concepts in some predetermined or wanted way. Under hyperknowledge representation we mean the identification (or approximation) of hyperknowledge-functions in order to exp...

Most of the real life classification problems have ill defined, imprecise or fuzzy class boundaries. Feedforward neural networks with conventional backpropagation learning algorithm are not tailored to this kind of classification problem. Hence, in this paper, feedforward neural networks, that use backpropagation learning algorithm with fuzzy objective functions, are investigated. A learning al...

In this paper are presented results obtained in a vowel recognition task applying fuzzy neural networks. The vowels, uttered from 10 speakers each in 1000 different contexts are recognized using as features the first three formant frequencies. The results obtained in this case show that fuzzyfication process improves the recognition rate of the classical variants. The paper is organized in 6 ch...

Network sensors consist of sensor nodes in which every node covers a limited area. The most common use ofthese networks is in unreachable fields.Sink is a node that collects data from other nodes.One of the main challenges in these networks is the limitation of nodes battery (power supply). Therefore, the use oftopology control is required to decrease power consumption and increase network acce...