The paper describes basic concepts of neural networks and fuzzy systems. It is shows that most commonly used neural network architecture of MLP – Multi Layer Perceptron is also one of the least efficient ones. Also most commonly used EBP – Error Back Propagation algorithm is not only very slow, but also it is not able to find solutions for optimal neural network architectures. EBP can solve pro...

Granular computing is a computational paradigm in which a granule represents a structure of patterns evolved by performing operations on the individual patterns. Two granular neural networks are described for performing the pattern analysis tasks like classification and clustering. The granular neural networks are designed by integrating fuzzy sets and fuzzy rough sets with artificial neural ne...

Nonlinear dynamic ball balancing beam has been successfully controlled by applying conventional methods, neural networks, and fuzzy logic respectively. Conventional methods necessitate strong mathematical and control background to derive equations. Neural networks learn to balance a ball, but the ball never settles down due to the fact that \discrete resolution of the boxes representation" was ...

In this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training eramples by a ...

The goal of this expository paper is to bring forth the basic current elements of soft computing (fuzzy logic, neural networks, genetic algorithms and genetic programming) and the current applications in intelligent control. Fuzzy sets and fuzzy logic and their applications to control systems have been documented. Other elements of soft computing, such as neural networks and genetic algorithms,...

Aircraft burst fault is uncertainty and ambiguity. Considering QAR data as the research object, the fault diagnosis system based on the T-S fuzzy neural network combined with aircraft maintenance processes is built. First, the system designs the network performance oversight function to improve genetic neural network program. Then the fuzzy logic is used to deal with fuzzy rules, which can dete...

Abstract. Artificial neural networks are powerful tools to learn functional relationships between data. They are widely used in engineering applications. Recurrent neural networks for fuzzy data have been introduced to map uncertain structural processes with deterministic or uncertain network parameters. Based on swarm intelligence, a new training strategy for neural networks is presented in th...

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...

In this paper, adaptive channel management approach fuzzy neural networks in heterogeneous wireless networks (ACM-FNN) is presented to efficient resource allocation, and admission control schemes are needed to guarantee quality-of-service (QoS) for differentiated services. The channel management in a two-tier such as micro cell or macro cell wireless networks. Effective reliability and efficien...

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...