Treffer: CFNN: Correlated fuzzy neural network

Title:
CFNN: Correlated fuzzy neural network
Authors:
MOHAMMAD MEHDI EBADZADEH, SALIMI-BADR, Armin
Source:
Neurocomputing (Amsterdam). 148:430-444
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 74 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Algorithme de Levenberg Marquardt, Levenberg Marquardt algorithm, Algoritmo de Levenberg Marquardt, Analyse donnée, Data analysis, Análisis datos, Analyse multivariable, Multivariate analysis, Análisis multivariable, Analyse régression, Regression analysis, Análisis regresión, Analyse statistique, Statistical analysis, Análisis estadístico, Essai statique, Static test, Ensayo estático, Fonction appartenance, Membership function, Función pertenencia, Fonction corrélation, Correlation function, Función correlación, Identification système, System identification, Identificación sistema, Logique floue, Fuzzy logic, Lógica difusa, Modélisation, Modeling, Modelización, Métrique, Metric, Métrico, Nombre flou, Fuzzy number, Número difuso, Optimisation, Optimization, Optimización, Processus Gauss, Gaussian process, Proceso Gauss, Prédiction linéaire, Linear prediction, Predicción lineal, Réseau neuronal, Neural network, Red neuronal, Système dynamique, Dynamical system, Sistema dinámico, Système expert, Expert system, Sistema experto, Système série, Series system, Sistema serie, Série temporelle, Time series, Serie temporal, Temps linéaire, Linear time, Tiempo lineal, Approximation d'une fonction, Function approximation, Aproximación de funciones, Réseau neuronal flou, Fuzzy neural nets, Red neuronal difusa, Structure réseau, Network structure, Estructura de redes, Système non conversationnel, Non interactive system, Sistema no interactivo, Correlated fuzzy rules, Fuzzy neural networks (FNN), Levenberg-Marquardt (LM) method, Mahalanobis distance, Nonlinear function approximation
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran, Islamic Republic of
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28844557
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.28844557
Database:
PASCAL Archive

Weitere Informationen

In this paper, a new fuzzy neural network model with correlated fuzzy rules (CFNN) based on the Levenberg―Marquardt (LM) optimization method is proposed. The proposed method is a new fuzzy network structure that is presented to approximate nonlinear functions especially the functions with high correlation between input variables with less number of fuzzy rules. A multivariable Gaussian fuzzy membership function is introduced that can consider the correlation between input variables and consequently it can model non-separable relations for interactive variables. The LM optimization method is used to learn parameters of both premise and consequent parts of the fuzzy rules. The suggested algorithm is successfully applied to seven tested examples including static function approximation, time-series prediction, non-linear dynamic system identification and a real-world complex regression problem. According to test observations; it can approximate nonlinear functions better than the other past algorithms with more compact structure and less number of fuzzy rules.